TensorFlow-Implementations

This repository contains the projects I have done with TensorFlow, and includes a few tutorials.

Check out my Youtube channel which uses some of this material for lessons.

Other social media sites:

TensorFlow / Deep Learning Tutorial: These are the files I have created to teach others Deep Learning:

MLP.pdf / MLP Part 2.pdf / CNN.pdf / RNN.pdf / Transformer.pdf / GNN.pdf
AlphaGo:Zero.pdf
Geometric Deep Learning.pdf (Summary and personal insights of Geometry Deep Learning proto-book https://geometricdeeplearning.com/ by Michael M. Bronstein, Joan Bruna, Taco Cohen, Petar Veličković)
Linear Regression in TensorFlow.ipynb
Fashion MNIST in TensorFlow.ipynb (Adapted from https://www.coursera.org/learn/introduction-tensorflow Week 1 Notebook)
Fashion MNIST (CNN) in TensorFlow.ipynb
GAT_GCN_node_classification.ipynb: Graph Neural Network implementation using TensorFlow on Cora Dataset
Tutorial_RNN_Text_Generation.ipynb
Custom Training Loop: Converts a simple Keras model.fit into an expanded customizable training loop using GradientTape. Includes graph plotting and model visualization utilities.
GPT_Demo.ipynb: Uses huggingface GPT-2 model for text generation, as well as Bertviz for attention visualization
ChatGPT - From Transformers to Reinforcement Learning from Human Feedback (RLHF).pdf: Introduction to how ChatGPT works

Paper_Reviews Folder:

RL Folder:

Overview: Documents Reinforcement Learning Experiments and Tutorials
RL Part 1.pdf, DQN.pdf
MCTS.ipynb: Using Monte Carlo / Monte Carlo Tree Search / Value Estimates to solve the game of Nim
Cart Pole.ipynb: Using rule-based and DQN to solve Cart Pole

Below are the projects I have done using TensorFlow (in Implementations folder):

Text Generation: Compares RNN vs a Markov Chain method to generate text. Surprisingly, both are around the same, just by using like n=5 previous characters to predict the next one for the Markov Chain method.
Text Generation for Code: Compares RNN vs a Markov Chain method to generate text for coding (and also news). Markov Chain methods generate better text, and that could be due to a very small dataset used. Datasets: news.txt, coding.txt
If-Else (Part 1): Investigates if a neural network can model if-else statements
Wordle: Solves the Wordle game using 5 different agents: Random (baseline), Minimize Worst Outcome, Maximize Entropy (Information Theory), Minimize Variance, Minimize Variance and Worst Outcome. Currently there are only algorithmic agents. In the future, there will also be Deep Q-Networks and Actor-Critic models.

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