From 0b506545aed8fb3923f521e740d897178f92f085 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Ezequiel=20Leonardo=20Casta=C3=B1o?= <14986783+ELC@users.noreply.github.com> Date: Wed, 18 Jun 2025 21:27:39 -0300 Subject: [PATCH] Scraped pydata-tel-avid-2024 MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Fixes #xxx Event config: ~~~yaml repo_dir: W:\Repositories\pyvideo-data # Copy the event template here and adapt to the event parameters # Only repo_dir: and events: are loaded # ============================================================================= events: # - title: PyData Virginia 2025 # dir: pydata-virginia-2025 # youtube_list: # - https://www.youtube.com/playlist?list=PLGVZCDnMOq0qLS7Mk-jI9jhb4t5UY6yDW # related_urls: # - label: Conference Website # url: https://pydata.org/virginia2025 # language: eng # dates: # begin: 2025-04-18 # end: 2025-04-19 # default: 2025-04-18 # minimal_download: false # issue: xxx # overwrite: # # all: true # takes 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a/pydata-tel-avid-2024/videos/alon-oring-a-shallow-introduction-to-self-attention-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/alon-oring-a-shallow-introduction-to-self-attention-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..bc42fbbdc --- /dev/null +++ b/pydata-tel-avid-2024/videos/alon-oring-a-shallow-introduction-to-self-attention-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "This presentation begins by laying out the foundational components of sequence modeling - Perceptrons and RNN cells. We discuss inherent issues associated with RNNs, focusing on challenges such as handling long sequences and managing vanishing or exploding gradients.\n\nAttention mechanisms form the core of this lecture. We present a practical case of attention in translation tasks, followed by an in-depth examination of self-attention, a variant independent of external context. We unpack its motivations and explain its implementation process. We proceed with an exploration of the Transformer model and the way it leverages self-attention. \n\nThe final section is dedicated to the Generative Pretrained Transformers (GPT) series. We break down the architecture of GPT assessing its distinguishing features. We delve into zero-shot, one-shot, and few-shot learning, discussing how these models interact with prompts with limited training examples.\n\nSpeaker bio:\nAlon Oring is the Head of Research at Dynamic Infrastructure, a predictive maintenance startup focused on using computer vision to identify defects and risks in critical infrastructure before they evolve into large-scale failures. Since joining Dynamic Infrastructure in 2019, Alon has led the development of several core technologies that obtained state-of-the-art performance and are currently serving multiple customers worldwide. Additionally, Alon is an active lecturer on deep learning, machine learning, and data science at Reichman University (IDC Herzliya), international coding boot camps, and an active mentor for up-and-coming data scientists.\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1582, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/HAKgbLcSiVg/maxresdefault.jpg", + "title": "Alon Oring: A Shallow Introduction to Self-Attention | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=HAKgbLcSiVg" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/aviv-vromen-optimizing-data-driven-decisions-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/aviv-vromen-optimizing-data-driven-decisions-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..3ae30894d --- /dev/null +++ b/pydata-tel-avid-2024/videos/aviv-vromen-optimizing-data-driven-decisions-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Optimizing Data-Driven Decisions: Introducing an Aggregation Engine for Efficient Feature Creation\n\nOne of the most common ways of using data to make informed, data-driven decisions, is creating features based on aggregated data. For example, the amount of transactions a client did in their bank account for the past 6 months can be aggregated into a feature that can be later used when making the decision of whether to approve or decline a new transaction request. A naive solution for implementing these aggregative features would be to iterate over large amounts of historical data, on some periodic and on-demand basis, to calculate relevant aggregations. The process that calculates the total transactions in the last 6 months, for example, would need to fetch the entire 6 months of transactions from scratch every time this calculation is performed.\nOur Aggregation Engine was designed to enable a better process, reusing historical aggregative data and preventing unnecessary recalculations of metrics. This engine enables processes to continuously calculate daily metric aggregations and store the values within a dedicated storage. The idea is to use these daily (stored) aggregations to calculate the final aggregation value and dramatically reduce the amount of fetched data required for the calculation as there will be no need to fetch all the historical data. Taking the previous example of a account\u2019s total transactions in the last 6 months, in the new solution we will no longer fetch 180 days of data every time the feature needs to be recalculated; instead, we will fetch only the last 180 daily aggregations (at the most 180 rows from a much shallower table) and sum them up to get the final calculation result.\n\nSpeaker Bio:\nAviv Vromen is an experienced ML and data infrastructure engineer with a strong background in Python. He is currently working at bluevine, where he has played a key role in the company's success in the financial technology sector. Prior to that, Aviv made a contributions as an algorithm developer at Rafael, focusing on complex multi-agent systems.\nIn his conference talk, Aviv aims to share his approach to using aggregated data in order to improve feature calculation.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1043, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/DHpFnQrcx_0/maxresdefault.jpg", + "title": "Aviv Vromen: Optimizing Data-Driven Decisions (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=DHpFnQrcx_0" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/daniel-goldfarb-adding-your-own-data-apps-to-jupyterlab-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/daniel-goldfarb-adding-your-own-data-apps-to-jupyterlab-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..248903037 --- /dev/null +++ b/pydata-tel-avid-2024/videos/daniel-goldfarb-adding-your-own-data-apps-to-jupyterlab-pydata-tel-aviv-2024.json @@ -0,0 +1,75 @@ +{ + "description": "JupyterLab enables you to work with documents and activities such as Jupyter notebooks, text editors, terminals, and custom components in a flexible, integrated, and extensible manner.\n\nThis is a practical talk about how to extend JupyterLab. It is for anyone who finds themselves doing complex or repetitive tasks and thinks that they, and others, may benefit from integrating those tasks into JupyterLab; in other words, it is for anyone who wants to extend JupyterLab.\n\nWe will walk through a step-by-step example of creating and adding an extension application to JupyterLab. As we proceed, we will discuss and demonstrate the tools and infrastructure available for extending JupyterLab. We'll learn, among other things, how to launch an app from different places within JupyterLab, how to style our app, and how to pass parameters to our app to modify its behavior. \n\nAttendees will take away a clear understanding of how JupyterLab extensions work, and what steps to take to build their own extensions. The step-by-step example will also be made available in a GitHub repository with tags for checking out each step in the process.\n\nOutline:\n- Introduction and a brief description of some JupyterLab extensions\n- Building a basic extension\n -- explanation of important files\n -- basic principles of extension tools and infrastructure\n -- installing and initializing our extension\n- Using the command registry\n- Executing from the Command Palette\n- Creating an app with widgets\n- Styling extensions in JupyterLab\n- Executing from the Launcher\n- Passing arguments to our application\n- Creating our own Sidebar launcher\n -- Using event listeners to launch different versions of our app\n- Summary\n- Q&A \n\nRequired background knowledge: \n- Attendees should be familiar with Jupyter Notebooks. \n- Basic knowledge of any typical object oriented programming language is also required.\n- (familiarity with [JupyterLab](https://telaviv2023.pydata.org/cfp/talk/3JNZUQ/) is helpful but not required). \n\nRepository: https://github.com/DanielGoldfarb/pydjlx\n\nSpeaker Bio:\nDaniel is an engineer at [Bloomberg](https://www.bloomberg.com/company/what-we-do/engineering-cto) with experience developing Trading Systems, Risk Analytics, and applications for Financial Analysis of Equities and Fixed Income securities. He holds a Ph.D. in Molecular Biophysics from the University of Virginia, and was a CFA charter holder and member of the Chartered Financial Analyst Institute for more than 10 years. He is the Open Source maintainer of [Matplotlib\u2019s MPLFINANCE package](https://github.com/matplotlib/mplfinance), and the author of McGraw-Hill\u2019s \u201c[Biophysics Demystified](https://www.amazon.com/dp/0071633642/).\u201d\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1643, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://telaviv2023.pydata.org/cfp/talk/3JNZUQ/", + "url": "https://telaviv2023.pydata.org/cfp/talk/3JNZUQ/" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://www.bloomberg.com/company/what-we-do/engineering-cto", + "url": "https://www.bloomberg.com/company/what-we-do/engineering-cto" + }, + { + "label": "https://github.com/matplotlib/mplfinance", + "url": "https://github.com/matplotlib/mplfinance" + }, + { + "label": "https://www.amazon.com/dp/0071633642/", + "url": "https://www.amazon.com/dp/0071633642/" + }, + { + "label": "https://github.com/DanielGoldfarb/pydjlx", + "url": "https://github.com/DanielGoldfarb/pydjlx" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Bm5yvtmRANo/maxresdefault.jpg", + "title": "Daniel Goldfarb: Adding Your Own Data Apps to JupyterLab | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Bm5yvtmRANo" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/ehud-karavani-causal-inference-with-causallib-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/ehud-karavani-causal-inference-with-causallib-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..0aedbd893 --- /dev/null +++ b/pydata-tel-avid-2024/videos/ehud-karavani-causal-inference-with-causallib-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Causal inference is the science of estimating the causal effects of actions using non-experimental data. \nIn this talk I will introduce causal inference, how it differs from the more familiar machine learning predictions, and why it is a harder task. I will present an overview of statistical models that can estimate causal effects, and I will present causallib - an open-source one-stop-shop Python package for flexible causal inference I created and maintain. \n\nThe main objective of the talk is to familiarize participants with the field of causal inference, increasing their awareness of the limitations in more common prediction models. A secondary objective is to present the tools that may help obtain causal inferences using a package which design corresponds with the scientific ecosystem in Python. \nThe talk is mainly aimed for data scientists familiar with machine learning, but group leaders may also benefit from understanding the limitations of regular prediction models and that they may be overcome.\n\nSpeaker Bio:\nEhud is a research staff member at IBM Research, marrying machine learning with causal inference to address questions in medicine and healthcare.\nHe combines applied research with tool development for research, having created and currently maintaining Causallib\u2014an open-source Python package for flexible causal inference modeling\u2014used by many practitioners in both academia and industry. Over his 8 years at IBM, he has led the causality strategy for the company's global efforts in drug discovery, consulted to many of its research labs worldwide, lectured on causality to staff and clients, developed novel methodologies and published his research.\nHe holds an MSc. in computer science and computational biology from the Hebrew University, where he worked on trait prediction using DNA and assessed its potential consequences for population genetics and embryo selection. A musician and hiker, but mostly a parent.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1553, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/7RUkcZEyhQM/maxresdefault.jpg", + "title": "Ehud Karavani: Causal inference with Causallib | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=7RUkcZEyhQM" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/eran-krakovsky-apl-inspired-techniques-for-advanced-numpy-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/eran-krakovsky-apl-inspired-techniques-for-advanced-numpy-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..f8da1bfeb --- /dev/null +++ b/pydata-tel-avid-2024/videos/eran-krakovsky-apl-inspired-techniques-for-advanced-numpy-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "The presentation will be organized into three sections:\n\n1. Introduction to APL and its Philosophy: We will start with a brief overview of APL and discuss its philosophy of array programming and manipulation, highlighting its influence on modern programming paradigms.\n\n2. NumPy Refresher and Common Pitfalls: We will recap the core concepts of NumPy and discuss common areas where programmers tend to struggle, such as broadcasting rules, axis manipulation, and memory management.\n\n3. Enhancing NumPy with APL Insights: In this part, we will dive deep into how principles from APL can be used to write more effective NumPy code. By focusing on vectorized operations and avoiding explicit loops, we can achieve substantial performance gains and cleaner code.\n\nSpeaker Bio:\nEran Krakovsky is a Senior Machine Learning Engineer @ AI21 Labs \n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1620, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/r8x9vmfGyrY/maxresdefault.jpg", + "title": "Eran Krakovsky: APL-Inspired Techniques for Advanced NumPy (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=r8x9vmfGyrY" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/eyal-gruss-let-our-optima-combine-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/eyal-gruss-let-our-optima-combine-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..b2ed86a9a --- /dev/null +++ b/pydata-tel-avid-2024/videos/eyal-gruss-let-our-optima-combine-pydata-tel-aviv-2024.json @@ -0,0 +1,59 @@ +{ + "description": "An introduction to solving combinatorial optimization and constraint satisfaction problems in Python. I will review the most popular libraries for SAT/CSP. We will then deep dive to a crash coarse on using Google's award winning OR-tools library, for efficiently solving some non-trivial real-world constrained combinatorial optimization problems.\n\nSpeaker Bio:\nDr. Eyal Gruss - Code/media/text artist, algorithms researcher, teaches computational creativity at the Holon institute of Technology. https://eyalgruss.com\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1737, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://eyalgruss.com", + "url": "https://eyalgruss.com" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/0gwp9ad2X4E/maxresdefault.jpg", + "title": "Eyal Gruss: Let our optima combine! | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=0gwp9ad2X4E" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/geva-kipper-identifying-repetitive-songs-using-lz-compression-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/geva-kipper-identifying-repetitive-songs-using-lz-compression-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..6ae581511 --- /dev/null +++ b/pydata-tel-avid-2024/videos/geva-kipper-identifying-repetitive-songs-using-lz-compression-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "To get the song lyrics we had to use some scraping, and we have plenty of tips to share on this.\nWe use our own implementation of LZ compression to measure how repetitive a song is, based on similar work that was done for English pop charts. We explain why this is a good idea, and what alternatives we considered.\nWe analyze some interesting trends in the radio pop charts using pandas and provide visualization using plotly, which we've also made available through an interactive web interface.\n\nSpeaker Bio:\nI'm an M.Sc graduate of Tel-Aviv University in Computer Science and I work for Google on products that aim to make phone calls more tolerable.\nI've been excited about data and algorithms since I was young, and what I like even more is trying to get other to be as excited about them as I am. \nI always try to pick projects that will interest the general public, or at least make them laugh.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1406, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jrKAtOsj1Lo/maxresdefault.jpg", + "title": "Geva Kipper: Identifying Repetitive Songs using LZ Compression (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jrKAtOsj1Lo" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/isan-rivkin-building-a-reproducible-rag-pipeline-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/isan-rivkin-building-a-reproducible-rag-pipeline-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..128e4b869 --- /dev/null +++ b/pydata-tel-avid-2024/videos/isan-rivkin-building-a-reproducible-rag-pipeline-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Building a Reproducible RAG Pipeline for a Q&A ChatBot with LangChain and Ollama\n\nAnyone in the data engineering space has been watching the development around LLMs (large language models). While LLMs represent a huge leap in AI capabilities, It\u2019s a rare case that they can provide commercial value without any additional work. Organizations can make the most of these models by adding their own data by using RAG or fine-tuning a model.\n\nIn this talk we\u2019ll dive into how the LLM model fine tuning vs extending with RAG approaches differ, what you need to know when employing each of these methods, and why reproducibility is important for both fine tuning and using RAG. This will be demoed through a real code example of the popular Python LangChain tool, Hugging Face Embeddings, Ollama\u2019s LLM, and the critical pieces that impact reproducibility\u2013\u2013git (code and environment) as well as lakeFS (data and model).\n\nSpeaker Bio:\nIsan Rivkin is R&D Team Leader in Treeverse, the company behind lakeFS, an open source platform that delivers resilience and manageability to object-storage based data lakes. Isan engineered and maintained petabyte-scale data infrastructure at analytics giant SmilarWeb.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1641, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/-ZR0gKl-glI/maxresdefault.jpg", + "title": "Isan Rivkin: Building a Reproducible RAG Pipeline (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=-ZR0gKl-glI" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/jonathan-harel-standup-comedy-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/jonathan-harel-standup-comedy-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..1ef06c2ff --- /dev/null +++ b/pydata-tel-avid-2024/videos/jonathan-harel-standup-comedy-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Speaker Bio:\nJonathan is a digital comedian - touching on everything that involves humor, technology and creativity. He is the cofounder of Fine, a company that helps developers build better software, faster; and the creator of \"Dark{mode}\": a docu-comedy web series that covers developer experience topics, trends, and best practices.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1186, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Ij4ulAfIa6A/maxresdefault.jpg", + "title": "Jonathan Harel: Standup Comedy (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Ij4ulAfIa6A" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/michael-ethan-levinger-securing-language-models-against-prompt-injection-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/michael-ethan-levinger-securing-language-models-against-prompt-injection-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..84f823143 --- /dev/null +++ b/pydata-tel-avid-2024/videos/michael-ethan-levinger-securing-language-models-against-prompt-injection-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Securing Language Models Against Prompt Injection with the Powerful LangChain Framework\n\nSpeaker Bio:\nMichael is a Data Scientist with over 4 years of experience, specializing in developing advanced algorithms for fraud prevention in the fintech industry. \n\nCurrently, I work as a Data Scientist within the risk department at Melio, a rapidly growing fintech company. \n\nAdditionally, I'm a mentor at Masterschool, where I work closely with my mentees to help them achieve their goals, stay motivated and on track.\n\nAlongside my work in data science, I'm also an avid ultra-marathon runner and a former coach. I believe that maintaining a healthy mind and body is essential for a fulfilling life and enjoy pushing myself to new physical and mental limits. \n\nI'm always looking for opportunities to collaborate and make a positive impact in the world. If you're interested in connecting with me or learning more about my work, feel free to send me a message!\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1422, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/jKP-lqs3ryY/maxresdefault.jpg", + "title": "Michael Ethan Levinger: Securing Language Models Against Prompt Injection(HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=jKP-lqs3ryY" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/mike-erlihson-state-space-models-and-deep-learning-is-there-a-new-revolution-on-our-doorstep.json b/pydata-tel-avid-2024/videos/mike-erlihson-state-space-models-and-deep-learning-is-there-a-new-revolution-on-our-doorstep.json new file mode 100644 index 000000000..e720fd0a1 --- /dev/null +++ b/pydata-tel-avid-2024/videos/mike-erlihson-state-space-models-and-deep-learning-is-there-a-new-revolution-on-our-doorstep.json @@ -0,0 +1,39 @@ +{ + "description": "State-space models (SSMs) have advanced from dynamic system tools to deep learning architectures like Mamba (S6),\nwhich combine parallel training with efficient inference for long sequences. This lecture covers SSMs' evolution and impact on sequential data modeling", + "duration": 2761, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/15jTs82U2SI/maxresdefault.jpg", + "title": "Mike Erlihson: State-Space Models and Deep Learning: Is there a new revolution on our doorstep?", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=15jTs82U2SI" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/mor-hananovitz-the-tl-dr-of-eda-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/mor-hananovitz-the-tl-dr-of-eda-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..5cdeb36e3 --- /dev/null +++ b/pydata-tel-avid-2024/videos/mor-hananovitz-the-tl-dr-of-eda-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "As the amount of data grows at an unprecedented rate, engineers face a critical challenge of efficiently processing and analyzing it. However, manual and time-consuming methods of familiarizing with data are still commonly used, despite the urgent need for shortcuts. In this talk, we review several automated EDA methods leveraging several Python libraries to research data, model it in distinct clusters and reduce time-to-insights.\nOur solution can be applied in various use cases, including data pre-processing for labeling, segmentation problems, and more.\nThis talk is an essential guide for lazy (and/or busy) engineers who want to streamline the process of data exploration and reduce the workload\n\nSpeaker Bio:\nHead of Data and Data scientists at Parazero, IoT and signal processing expert. \nCommunity lead and Mentor in WiDS. \nMSc mechanical engineering, researching fluid dynamic models.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1434, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/g5d8f3CUffU/maxresdefault.jpg", + "title": "Mor Hananovitz: The TL;DR of EDA (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=g5d8f3CUffU" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/moran-reznik-berttopic-from-free-text-feedbacks-to-calls-for-action-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/moran-reznik-berttopic-from-free-text-feedbacks-to-calls-for-action-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..29ace744c --- /dev/null +++ b/pydata-tel-avid-2024/videos/moran-reznik-berttopic-from-free-text-feedbacks-to-calls-for-action-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "This is a beginner level lecture, focused towards data analysts and product managers without string background in data science.\nWe\u2019ll start by describing the issue at hand: why traditional analysis tools are not good enough for free-text analysis tasks. Then we\u2019ll discuss how BertTopics works and show a minimal usage example in Python. We will dive into each of the stages of the process: embedding, clustering and dimensionality reduction and explore them.\nFinally, we\u2019ll show some results using interactive visualization.\n\nSpeaker Bio:\n2019-2020 data science intern at CheckPoint\n2020-2022 data analyst at Ebay\n2022-2023 senior data analyst at Lusha\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1463, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/uoM4tKywCiA/maxresdefault.jpg", + "title": "Moran Reznik: BertTopic, From Free-Text feedbacks to Calls for Action | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=uoM4tKywCiA" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/omri-fima-ibis-framework-making-data-science-work-at-any-scale-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/omri-fima-ibis-framework-making-data-science-work-at-any-scale-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..88cb96995 --- /dev/null +++ b/pydata-tel-avid-2024/videos/omri-fima-ibis-framework-making-data-science-work-at-any-scale-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Pandas and SQL are both great tools for data manipulation and analysis, yet they each come with their unique challenges. Pandas is handy and easy to use, especially for iterative research, but can slow down or crash with anything larger than a few gigabytes. SQL is efficient with huge datasets, but can be tricky and rigid when your analysis becomes more complex. \n\nIn our talk, we'll explore how these tools, instead of working against each other, can complement each other in your data science work. We'll show you how ibis can bring the python experience with the power of SQL environment, helping you get the best of both worlds.\n\nWe'll share our journey from complex distributed data science pipelines to jobs that can be run efficiently from a notebook. and share useful tips, common patterns and how to avoid frequent mistakes when working with ibis.\n\nJoin us as we show you how pandas and SQL can work together, making your data science tasks easier on any scale.\n\nSpeaker Bio:\nOmri is a Data Hacker, Maker, and LEGO builder. Currently, He is a Prinicipal Engineer at Walmart Global Tech\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1654, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/zXIivC1Hxys/maxresdefault.jpg", + "title": "Omri Fima: Ibis framework, Making data science work at any scale (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=zXIivC1Hxys" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/ortal-ashkenazi-unveiling-the-journey-of-nlp-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/ortal-ashkenazi-unveiling-the-journey-of-nlp-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..7a28f8700 --- /dev/null +++ b/pydata-tel-avid-2024/videos/ortal-ashkenazi-unveiling-the-journey-of-nlp-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Unveiling the Journey of Natural Language Processing (NLP): Milestones, Limitations, and Practical Applications\n\nJoin us in this introductory lecture on Natural Language Processing (NLP) as we delve into a captivating exploration of the key milestones that have shaped the advancement of this dynamic field. Delving into notable developments and breakthroughs, we will provide a comprehensive overview of NLP's evolution to its current state, while also shedding light on existing limitations and persistent challenges. Furthermore, we will explore the practical implications of NLP advancements in day-to-day research conduct. Whether you are a data scientist, researcher, or simply curious about NLP, this lecture will equip you with a fundamental understanding of significant milestones, limitations, and practical applications of NLP. By the end of the session, you will gain valuable insights into the dynamic world of NLP and its profound impact on the field of research.\n\n\nSpeaker Bio:\nOrtal Ashkenazi is a seasoned NLP researcher with over four years of experience at Gong, where she specialized in transforming data into actionable insights using advanced language models. Prior to that, she worked as a software engineer for 2.5 years at Algotec, developing information systems and processing solutions in the field of medical imaging. Holding an MSc from the Technion, her thesis focused on generating medical screening questionnaires by analyzing social media data, bridging artificial intelligence with practical healthcare solutions. Her expertise lies in model evaluation and deploying production-ready NLP solutions that address real-world challenges.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1356, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/QSOqFdff5_c/maxresdefault.jpg", + "title": "Ortal Ashkenazi: Unveiling the Journey of NLP | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=QSOqFdff5_c" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/ran-bar-zik-he-the-dangerous-data-anonymization-keynote-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/ran-bar-zik-he-the-dangerous-data-anonymization-keynote-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..93d618dd9 --- /dev/null +++ b/pydata-tel-avid-2024/videos/ran-bar-zik-he-the-dangerous-data-anonymization-keynote-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "In today's data-driven world, organizations and researchers often deal with vast amounts of sensitive information. Safeguarding individual privacy while harnessing the power of data for valuable insights has become an ethical imperative. Data anonymization is commonly employed to protect the identities of individuals and comply with privacy regulations. However, it is essential to recognize that data anonymization is not foolproof and may pose significant risks to users and companies if not handled with the utmost care.\nBy attending this session, participants will gain a comprehensive understanding of the challenges and dangers associated with data anonymization.\n\nSpeaker Bio:\nSenior software architect at CyberArk, journalist at The Marker, lecturer at Haifa university, author of 6 software development books, blogger at internet-israel.com\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1908, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/ST3phul7RA4/maxresdefault.jpg", + "title": "Ran Bar Zik (HE): The Dangerous Data Anonymization - Keynote | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=ST3phul7RA4" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/reuven-m-lerner-times-and-dates-in-pandas-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/reuven-m-lerner-times-and-dates-in-pandas-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..2fa8bbc38 --- /dev/null +++ b/pydata-tel-avid-2024/videos/reuven-m-lerner-times-and-dates-in-pandas-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Everything in Pandas comes down to the dtypes. And while most Pandas users are familiar with a variety of numeric and string dtypes, I've found that they're far less familiar with datetime and timedelta, two dtypes for working with dates and times. Pandas offers a great deal of functionality to work with them \u2014\u00a0and given how common it is to have date/time columns in our data, knowing how to work with them can be extremely useful.\n\nIn this talk, I'll introduce you to the datetime and timedelta dtypes, and particularly how we can read CSV data into Pandas in these forms. We'll see how you can sort and group your data using datetime values, and how you can extract pieces of datetime data with the \"dt\" accessor. We'll also see how you can create, manipulate, and compare values against \"timedelta\" values.\n\nThen we'll talk about indexes, and the host of functionality that we get when an index contains datetime values. We'll look at retrieving values with \"loc\", and also at the \"resample\" method that offers time-based grouping.\n\nBy the end of this talk, you'll have gained practical skills that you can apply to nearly any real-world data set you use.\n\nSpeaker Bio:\nReuven is a full-time trainer in Python and data science, teaching companies around the world via in-person, online, and recorded courses. He is the author of both \"Python Workout\" and \"Pandas Workout\" (Manning), and writes both \"Better Developers\" (weekly articles about Python) and \"Bamboo Weekly\" (weekly Pandas puzzles based on current events). Reuven lives with his wife and children in Modi'in, Israel.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1673, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/J-7xcs8nq7s/maxresdefault.jpg", + "title": "Reuven M. Lerner: Times and Dates in Pandas | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=J-7xcs8nq7s" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/roman-olshanskiy-david-katz-empowering-ml-developers-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/roman-olshanskiy-david-katz-empowering-ml-developers-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..7beafd206 --- /dev/null +++ b/pydata-tel-avid-2024/videos/roman-olshanskiy-david-katz-empowering-ml-developers-he-pydata-tel-aviv-2024.json @@ -0,0 +1,59 @@ +{ + "description": "Empowering ML Developers with Self Serve Data Analytics\n\nMobileye primarily focuses on algorithmic (ML/DL) development. Approximately 70% of our development time is dedicated to data-oriented tasks with large-scale data analysis being one of our major challenges. \nTo address this challenge, we've developed a solution that empowers developers to build data applications for large-scale data analysis using simple Python code which are then transformed into web dashboards. \nWe will present our system architecture and SDK that facilitates the apps development while abstracting the complexity of both visualization and writing queries over big data framework.\n\nSpeaker Bio:\nRoman Olshanskiy is with over 10 years of experience as a software engineer and leader in various domains, including backend, frontend, cloud computing, big data, and infrastructure, I am passionate about building innovative and efficient solutions, fostering collaboration, and bringing teams together to reach new heights.\n\nDavid Katz is a Software Engineer\u200b at Mobileye, specializing in self-service big data analytics platforms. \nWith extensive experience in big data engineering, he has a strong background in developing and optimizing large-scale data pipelines using Python, PySpark, and AWS. \nDavid has also contributed to open-source projects in the Python data ecosystem. (https://github.com/DavidKatz-il)\n\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1270, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://github.com/DavidKatz-il", + "url": "https://github.com/DavidKatz-il" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/tfOY0qTPbWs/maxresdefault.jpg", + "title": "Roman Olshanskiy & David Katz: Empowering ML Developers (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=tfOY0qTPbWs" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/ronny-ahituv-supercharging-ctr-click-through-rate-with-plug-and-play-ai-capabilities.json b/pydata-tel-avid-2024/videos/ronny-ahituv-supercharging-ctr-click-through-rate-with-plug-and-play-ai-capabilities.json new file mode 100644 index 000000000..3884da203 --- /dev/null +++ b/pydata-tel-avid-2024/videos/ronny-ahituv-supercharging-ctr-click-through-rate-with-plug-and-play-ai-capabilities.json @@ -0,0 +1,39 @@ +{ + "description": "explore a fully AI-driven pipeline designed to boost click-through rates (CTR) using adaptable, off-the-shelf tools. The pipeline leverages:\nGenerative AI and genetic algorithms for creating diverse ad creatives.\nContextual multi-armed bandits to select the best creatives based on real-time data, powered by built-in regressors.\nARIMA models to capture and adjust for seasonal trends.\nMultimodal embeddings to efficiently handle and cluster high-cardinality features.\nThis session will demonstrate how integrating readily available AI solutions can help achieve more effective, streamlined CTR optimization.", + "duration": 2221, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/MWwpe_yaNz8/maxresdefault.jpg", + "title": "Ronny Ahituv: Supercharging CTR (Click-Through Rate) with Plug-and-Play AI Capabilities", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=MWwpe_yaNz8" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/shirli-di-castro-shashua-ai-sql-and-graphql-walk-into-a-fertility-clinic-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/shirli-di-castro-shashua-ai-sql-and-graphql-walk-into-a-fertility-clinic-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..a937b4f15 --- /dev/null +++ b/pydata-tel-avid-2024/videos/shirli-di-castro-shashua-ai-sql-and-graphql-walk-into-a-fertility-clinic-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "AI, SQL, and GraphQL Walk into a Fertility Clinic\u2026 LLM-based Medical feature development\n\nSession outline:\n\n1. Main challenges of doctors while working with patients' medical data in databases. \n\n2. Goals of \u201cchatting with my data\u201d feature for fertility clinics. \n\n3. Going with the audience step by step on the architecture flow of this kind of feature - showing each step in the flow, what is the input and output of each step, what are our main concerns in every step \n\n4. Sharing with the audience the solutions we discussed in our team to achieve the feature goals, and that are adequate to the feature flow. Presenting the pros and cons of each solution with respect to: security, flexibility in input and output, development time and cost, explainability and reliability of answers given back to the doctor. In this part I share a Python code that includes LLM-based chains for each of the solutions. I show how the code enables us to seamlessly test each of the solutions and compare them. The three solutions are: LLM+SQL, LLM+GraphQL and LLM+RestAPI. \n\n5. Discussing why we chose the \u201cGraphQL solution\u201d, how we implement it and present additional challenges in this development section (leaving some of them not solved yet\u2026 :)) \n\n6. Summary\n\nSpeaker Bio:\nDr. Shirli Di-Castro Shashua is a professional in machine learning and AI technologies. She earned her PhD from the Technion in the Faculty of Electrical and Computer Engineering, specializing in reinforcement learning, following her BSc in Biomedical Engineering from Ben Gurion University. Currently, Shirli holds the role of Senior Data Scientist at Embie, where she develops innovative solutions to fertility clinics using advanced generative AI capabilities.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1635, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/Ud6oPwSpiuw/maxresdefault.jpg", + "title": "Shirli Di-Castro Shashua: AI, SQL, and GraphQL Walk into a Fertility Clinic | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=Ud6oPwSpiuw" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/tomer-doitshman-saas-compliance-automation-with-a-python-stack-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/tomer-doitshman-saas-compliance-automation-with-a-python-stack-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..9a3db2fbc --- /dev/null +++ b/pydata-tel-avid-2024/videos/tomer-doitshman-saas-compliance-automation-with-a-python-stack-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "AIOps for Security: SaaS Compliance Automation with a Python Stack\n\nAutomating the management of a large number of applications can be a daunting task, but the Python ecosystem offers exceptional tools to aid in compliance and security posture for SaaS apps. By utilizing AI to collect information from various sources, such as social media, continuous risk assessment for each application is possible, resulting in a comprehensive application catalog.\n\nDuring this talk, we will explore how we achieved this using a Python-based best-of-breed stack, which includes PyAthena and PySpark for querying, aggregating, and extracting pertinent information from the Hadoop stack. Additionally, we utilized the NLTK package in conjunction with Sklearn to analyze complex social media data using NLP techniques like stemming, tokenization, and classification to score sentiment through a Linear Regression model. Combining this sentiment analysis score with the application\u2019s network data and the Hadoop Stack with PySpark package enabled us to gain insights into the application. The analysis results and final insights were stored in MongoDB using PyMongo, which served as the primary database for the project.\n\nBy the end of this presentation, you will have a clear understanding of how to replicate this architecture and build your own AI-automation stack using Python, with a real-world example as a guide.\n\n\nSpeaker Bio:\nTomer is a security research team lead in Cato Research Labs at Cato Networks, with a keen interest in various aspects of cybersecurity, including reverse engineering, network protocol analysis, and detecting malicious traffic. Additionally, Tomer is enthusiastic about machine learning and thrives on tackling intricate challenges within this field. Presently, his main area of focus is network-based security research, where he endeavors to devise innovative approaches for detecting threats in corporate network \nsettings.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1286, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/ISxz4HKt9mI/maxresdefault.jpg", + "title": "Tomer Doitshman: SaaS Compliance Automation with a Python Stack (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=ISxz4HKt9mI" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/uri-menachem-using-row-groups-for-fast-filtering-of-large-parquet-files-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/uri-menachem-using-row-groups-for-fast-filtering-of-large-parquet-files-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..42c006691 --- /dev/null +++ b/pydata-tel-avid-2024/videos/uri-menachem-using-row-groups-for-fast-filtering-of-large-parquet-files-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "The Apache parquet file format offers convenient columnar-based data access and is popular in both Python-based (i.e., Pandas) and big data frameworks. In this talk, we will dive into the details of how parquet files are saved, advantages and disadvantages of this format, and how the Row Groups feature can solve performance challenges and reduce cloud costs, providing a good balance between column and row-wise access (necessary in cloud object storage solutions such as AWS S3, for example). In addition to discussing some technical background, we will also demonstrate how it can be implemented efficiently.\n\nSpeaker Bio:\nMenachem Kluft - Senior Backend Developer in Mobileye.\nUri Mogilevsky - Technical lead in Mobileye.\nWe're part of a group creating a data processing infrastructure. This system serves different groups, addresses various needs, and tackles challenging data processing tasks across the entire company.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1775, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/eXdfYiJmMUQ/maxresdefault.jpg", + "title": "Uri & Menachem: Using Row Groups for fast filtering of large parquet files (HE)|PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=eXdfYiJmMUQ" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/yoav-nordmann-processing-biggish-data-with-duckdb-and-python-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/yoav-nordmann-processing-biggish-data-with-duckdb-and-python-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..b4242b76a --- /dev/null +++ b/pydata-tel-avid-2024/videos/yoav-nordmann-processing-biggish-data-with-duckdb-and-python-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Did you ever wonder: \"What if there was a tool to handle biggish data?\" You do not have terabytes of data, but using just Python with Pandas didn't quite work, and now you are using tools like Apache Spark and Trino with all their complexity. And what the hell is biggish data anyway? \nIt is time to declare: Not all Big Data is created equal, and therefore, not all Big Data needs the same tools to process and query. \nFirst I want to explain what biggish data is. Then, I will show you how incorporating DuckDB into your Python project, you can relinquish all those complex data analysis and processing tools when dealing with biggish data. Let's delve together into the many possibilities DuckDB offers using Python when processing data, and I hope you will see how this opens a whole new set of options for you.\n\nSpeaker Bio:\nYoav Nordmann is a Backend & Data Architect and Tech Lead with over 20 years of experience. At Tikal he holds the position of a Group Leader mentoring fellow workers. He is passionate about new and emerging technologies, knowledge sharing and a fierce advocate for open source. Being in the industry for so long gives him a sense of perspective on different languages, architectures, and hypes.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1497, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/wjhMB4XhQxQ/maxresdefault.jpg", + "title": "Yoav Nordmann: Processing Biggish Data with DuckDB and Python (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=wjhMB4XhQxQ" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/yoel-shuki-chatgpt-goes-beyond-its-knowledge-cut-off-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/yoel-shuki-chatgpt-goes-beyond-its-knowledge-cut-off-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..b8b1f6a5f --- /dev/null +++ b/pydata-tel-avid-2024/videos/yoel-shuki-chatgpt-goes-beyond-its-knowledge-cut-off-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "Live Coding: ChatGPT Goes Beyond Its Knowledge Cut-Off With External Database Integration\n\nYou've all heard about ChatGPT, one of the most advanced language models out there \ud83e\udd16. Unfortunately, because the model was trained using the data available at its training time, it may not be aware of the most recent developments \ud83d\ude22\n\nJoin us for a live coding session, where we'll utilize TechCrunch as a source for up-to-date information \ud83d\uddde\ufe0f. We'll scrape it and embed the articles into a high-dimensional vector space. Now, when a user asks a question, instead of solely relying on ChatGPT\u2019s knowledge, we'll retrieve the most relevant item from the TechCrunch database (using vector similarity) and pass it to ChatGPT.\n\nFinally, one of the most advanced language models will advance to November 2024! \ud83d\udcaa\ud83c\udffb\n\nSpeaker Bio:\nShuki is a seasoned Data Scientist with an emphasis on NLP, classical ML, visualization, and experimentation.\nDriven by a great passion for the field, He is inspired by unintuitive insights and inferences made by smart algorithms. In his talks, he tries to convey my typical spirit and enthusiasm while delivering crisp takeaways.\n\nYoel has over 14 years of experience as a software engineer and algorithm developer in various domains, including NLP, recommender systems, vision, and cybersecurity.\nHe is passionate about good quality code, interesting ideas and sophisticated algorithms. He loves encountering elegant equations while trying to solve real-life problems.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1685, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/n_mcq5ewMms/maxresdefault.jpg", + "title": "Yoel & Shuki: ChatGPT Goes Beyond Its Knowledge Cut-Off (HE) | PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=n_mcq5ewMms" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/yonathan-guttel-2d-arima-capturing-new-trends-for-distant-time-horizons-he-pydata-tel-aviv-2024.json b/pydata-tel-avid-2024/videos/yonathan-guttel-2d-arima-capturing-new-trends-for-distant-time-horizons-he-pydata-tel-aviv-2024.json new file mode 100644 index 000000000..96efd56bc --- /dev/null +++ b/pydata-tel-avid-2024/videos/yonathan-guttel-2d-arima-capturing-new-trends-for-distant-time-horizons-he-pydata-tel-aviv-2024.json @@ -0,0 +1,55 @@ +{ + "description": "2D ARIMA: Capturing New Trends for Distant Time Horizons in Cohort Revenue Forecasting\n\nWe are proud to introduce the \"Two-Dimensional ARIMA\" method - a novel technique we designed to enhance ARIMA models for today's forecasting demands.\nThis approach elevates the capabilities of traditional ARIMA by adeptly capturing emerging trends for extended forecasting horizons in cohort revenue predictions.\nIn this presentation, we'll illuminate its core principles:\nVersatility: Crafted for any cohort revenue forecasts.\nResponsiveness: Incorporating both recent and long-term data for swift market response.\nSynergy: time-centric elements, seasonal variations, and overarching trends to produce a cohesive forecast.\n\nSpeaker Bio:\nYonathan Guttel is a Data Scientist at Lightricks, serving in the Business DS team. In this role, he aids the marketing and finance sectors by crafting models, tools, and pipelines, refining revenue forecasts and marketing strategies.\n\n\nFollow PyData Tel Aviv on:\nhttps://www.meetup.com/PyData-Tel-Aviv/\nhttps://www.linkedin.com/company/pydata-tlv\nhttps://www.facebook.com/PyDataTLV\nhttps://x.com/PyDataTLV\n\nPyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United States. PyData provides a forum for the international community of users and developers of data analysis tools to share ideas and learn from each other. The global PyData network promotes discussion of best practices, new approaches, and emerging technologies for data management, processing, analytics, and visualization. PyData communities approach data science using many languages, including (but not limited to) Python, Julia, and R.", + "duration": 1272, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + }, + { + "label": "https://www.meetup.com/PyData-Tel-Aviv/", + "url": "https://www.meetup.com/PyData-Tel-Aviv/" + }, + { + "label": "https://www.linkedin.com/company/pydata-tlv", + "url": "https://www.linkedin.com/company/pydata-tlv" + }, + { + "label": "https://x.com/PyDataTLV", + "url": "https://x.com/PyDataTLV" + }, + { + "label": "https://www.facebook.com/PyDataTLV", + "url": "https://www.facebook.com/PyDataTLV" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/97nfRfn1JS4/maxresdefault.jpg", + "title": "Yonathan Guttel: 2D ARIMA, Capturing New Trends for Distant Time Horizons(HE)| PyData Tel Aviv 2024", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=97nfRfn1JS4" + } + ] +} diff --git a/pydata-tel-avid-2024/videos/yuval-feinstein-georgia-on-my-mind-nlp-meets-social-network-analysis-for-exploring-new-domains.json b/pydata-tel-avid-2024/videos/yuval-feinstein-georgia-on-my-mind-nlp-meets-social-network-analysis-for-exploring-new-domains.json new file mode 100644 index 000000000..3c6404d5e --- /dev/null +++ b/pydata-tel-avid-2024/videos/yuval-feinstein-georgia-on-my-mind-nlp-meets-social-network-analysis-for-exploring-new-domains.json @@ -0,0 +1,39 @@ +{ + "description": "How do you choose your focus when entering a new domain?\nI suggest a method combining social network analysis (SNA) with natural language processing (NLP).\nWe'll utilize the networkx, spaCy and wikipedia Python packages to get from search terms to insights.", + "duration": 2188, + "language": "eng", + "recorded": "2024-11-04", + "related_urls": [ + { + "label": "Conference Website", + "url": "https://pydata.org/telaviv2024/" + } + ], + "speakers": [ + "TODO" + ], + "tags": [ + "Education", + "Julia", + "NumFOCUS", + "Opensource", + "PyData", + "Python", + "Tutorial", + "coding", + "how to program", + "learn", + "learn to code", + "python 3", + "scientific programming", + "software" + ], + "thumbnail_url": "https://i.ytimg.com/vi/5g7lZuejYHo/maxresdefault.jpg", + "title": "Yuval Feinstein: Georgia on my Mind: NLP Meets Social Network Analysis for Exploring New Domains", + "videos": [ + { + "type": "youtube", + "url": "https://www.youtube.com/watch?v=5g7lZuejYHo" + } + ] +}