A framework for few-shot evaluation of language models.

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

🦜🔗 Build context-aware reasoning applications

Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.

CLI platform to experiment with codegen. Precursor to: https://lovable.dev

😎 A curated list of awesome MLOps tools

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Code and files to go along with CS329s machine learning model deployment tutorial.

Open-source serverless enterprise CMS. Includes a headless CMS, page builder, form builder, and file manager. Easy to customize and expand. Deploys to AWS.

Machine Learning Toolkit for Kubernetes

For extensive instructor led learning

Content for Udacity's Machine Learning curriculum

Repo for the Deep Learning Nanodegree Foundations program.

Projects and exercises for the Udacity Intro to Machine Learning with TensorFlow course

Repo for the Deep Reinforcement Learning Nanodegree program

Utilities for working with image data, text data, and sequence data.

The goal of CLAIMED is to enable low-code/no-code rapid prototyping style programming to seamlessly CI/CD into production.

A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"

Curated list of project-based tutorials

TensorFlow - A curated list of dedicated resources http://tensorflow.org

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

A Code-First Introduction to NLP course

Lab materials for the Full Stack Deep Learning Course

Python assignments for the machine learning class by andrew ng on coursera with complete submission for grading capability and re-written instructions.

Deep Learning Tutorial notes and code. See the wiki for more info.

Highly cited and useful papers related to machine learning, deep learning, AI, game theory, reinforcement learning

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 ;)

Translation of <Machine Learning Yearning> by Andrew NG

A collection of various deep learning architectures, models, and tips