ML_with_PyTorch

A structured and comprehensive collection of PyTorch notebooks and projects, covering machine learning fundamentals, computer vision, and advanced AI. This repository takes users from core concepts to practical projects, including CNN-based trading classifiers, multi-agent AI systems, and transformer-based large language models.

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📂 Folder Structure

• Pytorch_DeepLearning/ — Core PyTorch notebooks from fundamentals to advanced applications
  • 00_Fundamentals/ — PyTorch basics and ML workflows
    • 00_pytorch_fundamentals.ipynb
    • 01_pytorch_workflow.ipynb
    • 02_ML_&_Classifications.ipynb
  • 01_Computer_Vision/ — Computer vision examples and CNN models
    • 03_pytorch_computer_vision.ipynb
    • 04_pytorch_Custom_Dataset_model.ipynb
    • candlestick_pattern_recognition_fundamentals_to_advanced.ipynb
      CNN-based candlestick pattern recognition from preprocessing to evaluation
• transformer_LLM_from_scratch/ — Full pipeline for building a transformer-based large language model from a base model
• Other_projects/ — Experimental and miscellaneous projects

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🚀 Notable Projects

Candlestick Pattern Recognition

• Classifies candlestick chart images using Convolutional Neural Networks (CNNs)
• Covers preprocessing, data augmentation, model training, and evaluation
• Demonstrates practical application of computer vision in algorithmic trading

Multi-Agent AI

• Implements multiple agents working collaboratively in an environment for efficient learning
• Demonstrates advanced reinforcement learning techniques and environment setup

Transformer LLM

• Builds a transformer-based large language model from a base model
• Covers tokenization, attention mechanisms, training, and evaluation
• Includes a complete pipeline to fine-tune the model for domain-specific tasks

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📦 Requirements

Install dependencies via: pip install pytorch