Repository Index

T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis

• neural-network
• machine-learning
• tensorflow
• keras

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

• deep-learning
• neural-network
• convolutional-networks
• pytorch

Create delightful software with Jupyter Notebooks

• jupyter-notebooks
• python
• literate-programming
• fastai

Reference models and tools for Cloud TPUs.

Technical Analysis Library using Pandas and Numpy

• trading
• technical-analysis
• python3
• volume

Sutskever 30 implementations inspired by https://papercode.vercel.app/ | For Agents, use https://github.com/pageman/Sutskever-Agent | Polyglot / Multi-Backed version at https://github.com/pageman/sutskever-30-beyond-numpy

Repo for the Deep Reinforcement Learning Nanodegree program

• deep-reinforcement-learning
• reinforcement-learning
• reinforcement-learning-algorithms
• neural-networks

links to conference publications in graph-based deep learning

• graph-neural-networks
• graph-convolutional-networks
• graph
• deep-learning

Translated🎓 Academic portfolio that boosts citations. AI generates pages, you own as Markdown. BibTeX auto-import, Jupyter, LaTeX, slides, visual block editor — free to host forever. Academic homepage, AI-generated, Markdown-owned 👇

• hugo
• website-generation
• academic
• netlify

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

• dependency-graph

ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

• machine-learning
• economics
• causal-inference
• causality

A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep

• data-science
• machine-learning
• deep-learning
• data-portfolio

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning

• reinforcement-learning
• machine-learning
• artificial-intelligence
• deep-reinforcement-learning

Translated"PyTorch Practical Tutorial" (Second Edition): whether you are starting from zero, working on CV, NLP, or LLM project applications, or moving on to advanced engineering deployment, it's all here. We believe that with the help of this book, readers will be able to master the use of PyTorch with ease and become an excellent deep learning engineer.

• computer-vision
• deepsort
• diffusion-models
• onnx

Python code for common Machine Learning Algorithms

• linear-regression
• polynomial-regression
• logistic-regression
• decision-trees

A better notebook for Scala (and more)

• notebook
• scala

A 15TB Collection of Physics Simulation Datasets

TranslatedWith only a Python foundation, build your own embodied intelligence robot from scratch; step by step from scratch, build VLA/OpenVLA/SmolVLA/Pi0, and gain a deep understanding of embodied intelligence

• embodied-agent
• embodied-ai
• embodied-intelligence
• openvla

Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation

• imitation-learning
• robotics

Solve puzzles. Improve your pytorch.

• machine-learning
• puzzle
• torch

Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.

• tensorflow
• machine-learning
• regression
• convolutional-neural-networks

🎨🖌 Modern, bright color theme for Sublime Text

• sublime-text
• dark-theme
• light-theme
• theme

Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

• deep-learning
• coursera
• coursera-assignment
• coursera-specialization

Learn Agentic AI using Dapr Agentic Cloud Ascent (DACA) Design Pattern and Agent-Native Cloud Technologies: OpenAI Agents SDK, Memory, MCP, A2A, Knowledge Graphs, Dapr, Rancher Desktop, and Kubernetes.

• agentic-ai
• langmem
• mcp
• openai-api