afshinea / stanford-cs-221-artificial-intelligence
VIP cheatsheets for Stanford's CS 221 Artificial Intelligence
☆2,594Updated 4 years ago
Related projects ⓘ
Alternatives and complementary repositories for stanford-cs-221-artificial-intelligence
- VIP cheatsheets for Stanford's CS 230 Deep Learning☆6,350Updated 4 years ago
- VIP cheatsheets for Stanford's CS 229 Machine Learning☆17,645Updated 4 years ago
- VIP cheatsheets for Stanford's CME 106 Probability and Statistics for Engineers☆667Updated 4 years ago
- A guideline for building practical production-level deep learning systems to be deployed in real world applications.☆4,346Updated 11 months ago
- Translation of VIP cheatsheets for Machine Learning Deep Learning, and Artificial Intelligence☆892Updated last year
- Template for data generator in Keras☆284Updated 6 years ago
- Notes on the Deep Learning book from Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016)☆1,782Updated 4 years ago
- VIP cheatsheets for Stanford's CME 102 Ordinary Differential Equations for Engineers☆236Updated 4 years ago
- Lab materials for the Full Stack Deep Learning Course☆1,205Updated 2 years ago
- Lab Materials for MIT 6.S191: Introduction to Deep Learning☆7,251Updated 3 months ago
- https://huyenchip.com/ml-interviews-book/☆3,433Updated 4 months ago
- A repo for data science related questions and answers☆2,413Updated 2 years ago
- A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"☆9,122Updated last year
- Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library☆595Updated 4 years ago
- The Python code to reproduce the illustrations from The Hundred-Page Machine Learning Book.☆1,809Updated 4 months ago
- This repository is to prepare for Machine Learning interviews.☆1,481Updated 5 years ago
- ☆2,540Updated 2 years ago
- Answers to 120 commonly asked data science interview questions.☆3,717Updated 9 months ago
- The 3rd edition of course.fast.ai☆4,897Updated 5 months ago
- A crash course in six episodes for software developers who want to become machine learning practitioners.☆2,787Updated 6 months ago
- In this repository, I will share some useful notes and references about deploying deep learning-based models in production.☆4,307Updated this week
- Research papers with annotations, illustrations and explanations☆829Updated 3 years ago
- 📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.☆2,823Updated last year
- A Code-First Introduction to NLP course☆3,425Updated last year
- Full Stack Deep Learning Online Course☆889Updated 3 years ago
- Study guides for MIT's 15.003 Data Science Tools☆1,794Updated 4 years ago
- Course material for STAT 479: Machine Learning (FS 2019) taught by Sebastian Raschka at University Wisconsin-Madison☆702Updated 3 years ago
- PyTorch tutorials and best practices.☆1,656Updated 2 years ago
- Machine learning glossary☆3,014Updated 3 months ago
- Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course☆10,284Updated 6 months ago