First-principle implementations of various AI algorithms using a wide range of deep learning frameworks, accompanied by relevant research papers
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Updated
Oct 8, 2024 - Jupyter Notebook
First-principle implementations of various AI algorithms using a wide range of deep learning frameworks, accompanied by relevant research papers
Basic neural network library
A Neural Network implementation from scratch built in Rust, designed to work with the MNIST dataset of handwritten digits.
Tiny neural network library with automatic differentiation.
My solutions for "SD-TSIA210-Machine Learning" laboratories, a.y. 2023-2024 - Télécom Paris.
Nuclei Microcontroller Software Interface Standard Development Repo
Frontend to build, test and compare various NN configs
Master's thesis project, combining topic clustering, sentiment analysis, optimisation and stock trading RNN's
Reinforcement Learning project written from scratch, entirely in C++, without any Machine Learning library, to teach a Neural Network to play the game of Snake
The neural network is more selective.
This notebook is designed to train a deep learning model for face emotion recognition. It uses TensorFlow and is run in a Google Colab environment.
A toy NN library for JS to play and learn NN concepts.
Computer Science Notes! PyTorch,TF,ETC.
📈 Stock Prediction Research: An undergraduate's exploration of algorithms and techniques, including LSTM neural networks, to predict stock market trends. A work in progress with room for growth and collaboration. 🎓📊
This project demonstrates the process of training a neural network to fit various mathematical functions
Predicts the secondary structures of proteins based on amino acid sequences, using feed-forward NN, SVM, and CNN approaches.
Interpreter/Compiler for a made up language
A reinforcment learning approach to the car racing problem in openai gym.
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