Add Pydata Tel Avid 2024

pydata-tel-avid-2024/category.json

@@ -0,0 +1,3 @@
+{
+  "title": "PyData Tel Aviv 2024"
+}

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"
+    }
+  ]
+}

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"
+    }
+  ]
+}

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"
+    }
+  ]
+}

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"
+    }
+  ]
+}

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"
+    }
+  ]
+}