Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Last update: Dec 28, 2022

Overview

Google Cloud AI Developer Relations

This repository contains content produced by Google Cloud AI Developer Relations for machine learning and artificial intelligence. The content covers a wide spectrum from educational, training, and research, covering from novices, junior/intermediate to advanced.

The content has been designed both for self-learning, and for being incorporating into instructional material for universities, private coding schools, and professional training. Licensed as Apache 2.0 and CC-BY, organizations are free to integrate and customize the material into their curriculum.

The repository is organized by:

Directory	Description
`handbooks`	Google (FREE) PDF softcopy of handbooks - CC-BY 4.0 license (2019)
`books`	PDF softcopy of books - preview versions
`workshops`	workshops (presentation slides and code labs) - CC-BY 4.0 license (2019)
`notebooks`	notebooks for production-grade solutions
`zoo`	tf.keras model zoo (coded in idiomatic and composable design pattern) - Apache 2.0 license
`community labs`	Labs for community participation in research

Reviewers and Contributors

We thank the following for their reviews and contributions to the Idiomatic Programmer:

Google Cloud AI - Developer Relations

Andrew Ferlitsch
Noah Negrey
Yu-Han Liu
Shahin Saadati
Torry Yang
Gonzalo Gasca Meza
Amy Unruh
Martin Gorner
Brad Miro
Tianzi Cai
Sara Robinson
Puneith Kaul
Crystal Gomes

Other Current/Former Googlers

William Barret
Sharon Maher

Google Developer Experts (GDE)

Margaret Maynard-Reid / Seattle

ML Practioners

Hobson Lane
Enoch Tetteh
Bill Liu
Miklos Toth

Disclaimer

This is not an officially supported Google product.

Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

Related tags

Overview

Google Cloud AI Developer Relations

Reviewers and Contributors

Disclaimer

Owner

Google Cloud Platform

Powerful unsupervised domain adaptation method for dense retrieval.

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Reading Group @mila-iqia on Computational Optimal Transport for Machine Learning Applications

The Multi-Mission Maximum Likelihood framework (3ML)

Examples of using f2py to get high-speed Fortran integrated with Python easily

Implementation of "Fast and Flexible Temporal Point Processes with Triangular Maps" (Oral @ NeurIPS 2020)

Wordplay, an artificial Intelligence based crossword puzzle solver.

A DNN inference latency prediction toolkit for accurately modeling and predicting the latency on diverse edge devices.

Lexical Substitution Framework

Cycle Consistent Adversarial Domain Adaptation (CyCADA)

Official PyTorch Implementation of Hypercorrelation Squeeze for Few-Shot Segmentation, arXiv 2021

Bonnet: An Open-Source Training and Deployment Framework for Semantic Segmentation in Robotics.

Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

FocusFace: Multi-task Contrastive Learning for Masked Face Recognition

This repository contains the implementations related to the experiments of a set of publicly available datasets that are used in the time series forecasting research space.

DuBE: Duple-balanced Ensemble Learning from Skewed Data

Numenta Platform for Intelligent Computing is an implementation of Hierarchical Temporal Memory (HTM), a theory of intelligence based strictly on the neuroscience of the neocortex.

OpenMMLab 3D Human Parametric Model Toolbox and Benchmark

An optimization and data collection toolbox for convenient and fast prototyping of computationally expensive models.

Relaxed-machines - explorations in neuro-symbolic differentiable interpreters