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Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Learn how to design, develop, deploy and iterate on production-grade ML applications.
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 ;)
The fastai book, published as Jupyter Notebooks
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filte…
Materials for the Learn PyTorch for Deep Learning: Zero to Mastery course.
Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course
Automatic extraction of relevant features from time series:
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K…
Python code for "Probabilistic Machine learning" book by Kevin Murphy
TensorFlow 2.x version's Tutorials and Examples, including CNN, RNN, GAN, Auto-Encoders, FasterRCNN, GPT, BERT examples, etc. TF 2.0版入门实例代码，实战教程。
A course in reinforcement learning in the wild
A collection of infrastructure and tools for research in neural network interpretability.
This project reproduces the book Dive Into Deep Learning (https://d2l.ai/), adapting the code from MXNet into PyTorch.
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN. Classifying the type of movement amongst six activity categories - Guillaume Chevalier
YoloV3 Implemented in Tensorflow 2.0
Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.
Data Augmentation For Object Detection
The Programmable Cypher-based Neuro-Symbolic AGI that lets you program its behavior using Graph-based Prompt Programming: for people who want AI to behave as expected
LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
Estimates the size of a PyTorch model in memory
Gradient based receptive field estimation for Convolutional Neural Networks
Practical Exercises in TensorFlow 2.0 for Ian Goodfellows Deep Learning Book