Skip to content

README.md

Latest commit

 

History

History
60 lines (44 loc) · 1.93 KB

README.md

File metadata and controls

60 lines (44 loc) · 1.93 KB

Deep Learning & AI Methods

A curated collection of Jupyter notebooks featuring implementations of foundational and advanced algorithms in deep learning, reinforcement learning, and probabilistic modeling.

Overview

This repository serves as a comprehensive reference for core AI/ML techniques, with implementations built from first principles using NumPy and PyTorch. Each module includes detailed explanations, mathematical derivations, and practical examples.

Repository Structure

Directory Description
deep_learning/ Neural network architectures including autoencoders, RNNs, LSTMs, and dimensionality reduction
probabilistic_models/ Bayesian networks, inference algorithms, and parameter/structure learning
reinforcement_learning/ RL algorithms from bandits to policy gradients and deep RL

Topics Covered

Deep Learning

  • Autoencoders & Variational Autoencoders (VAE)
  • Recurrent Neural Networks (RNN) & LSTM
  • Real-Time Recurrent Learning (RTRL)
  • t-SNE Dimensionality Reduction

Probabilistic Models

  • Bayesian Network Inference
  • Hidden Markov Models (HMM)
  • Approximate Inference (Rejection Sampling, Likelihood Weighting)
  • Parameter & Structure Learning

Reinforcement Learning

  • Multi-Armed Bandits
  • Dynamic Programming (Value/Policy Iteration)
  • Monte Carlo Methods
  • Temporal Difference Learning (SARSA, Q-Learning)
  • Deep Q-Networks (DQN)
  • Policy Gradient Methods & PPO
  • Imitation Learning (DAgger)

Requirements

  • Python 3.8+
  • PyTorch
  • NumPy
  • Jupyter Notebook
  • Additional dependencies as specified in individual notebooks

Usage

Clone the repository and navigate to the desired topic:

git clone https://github.com/username/deep_learning.git
cd deep_learning
jupyter notebook

License

This project is licensed under the MIT License. See LICENSE for details.