Stars
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
Interact with your documents using the power of GPT, 100% privately, no data leaks
Repo of the code from the Medium article
Python Implementation of Reinforcement Learning: An Introduction
Learning to trade under the reinforcement learning framework
Repo for the Deep Reinforcement Learning Nanodegree program
A complete computer science study plan to become a software engineer.
A clean and concise Python implementation of SIFT (Scale-Invariant Feature Transform)
ML001 Sources Code and Learning Materials
The fastai book, published as Jupyter Notebooks
🦀 Small exercises to get you used to reading and writing Rust code!
🎇 Quickly search over billions of images
Ultimate Solidity, Blockchain, and Smart Contract - Beginner to Expert Full Course | Python Edition
NeuPy is a Tensorflow based python library for prototyping and building neural networks
📚 Freely available programming books
Conditional GAN for generating synthetic tabular data.
Using deep stacked residual bidirectional LSTM cells (RNN) with TensorFlow, we do Human Activity Recognition (HAR). Classifying the type of movement amongst 6 categories or 18 categories on 2 diffe…
Google Research
Learn fast, scalable, and calibrated measures of uncertainty using neural networks!
Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
Implementing Bayes by Backprop
aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
tableGAN is a synthetic data generation technique (Data Synthesis based on Generative Adversarial Networks paper) based on Generative Adversarial Network architecture (DCGAN).