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

the code for paper "Energy-Based Open-World Uncertainty Modeling for Confidence Calibration"

An index of algorithms for learning causality with data

Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

Practical Blind Denoising via Swin-Conv-UNet and Data Synthesis

Adversarial Learning for Modeling Human Motion

Code for CVPR2019 paper《Unequal Training for Deep Face Recognition with Long Tailed Noisy Data》

Unofficial PyTorch implementation of TokenLearner by Google AI

ObjectDetNet is an easy, flexible, open-source object detection framework

Code for ACM MM 2020 paper "NOH-NMS: Improving Pedestrian Detection by Nearby Objects Hallucination"

Python script for performing depth completion from sparse depth and rgb images using the msg_chn_wacv20. model in ONNX

This repository collects project-relevant Isabelle/HOL formalizations.

Official implementation of Deep Reparametrization of Multi-Frame Super-Resolution and Denoising

[ACM MM 2021] Multiview Detection with Shadow Transformer (and View-Coherent Data Augmentation)

PyTorch implementation of ENet

Dataset used in "PlantDoc: A Dataset for Visual Plant Disease Detection" accepted in CODS-COMAD 2020

Official implementation for Scale-Aware Neural Architecture Search for Multivariate Time Series Forecasting

NitroFE is a Python feature engineering engine which provides a variety of modules designed to internally save past dependent values for providing continuous calculation.

Differentiable scientific computing library

for a paper about leveraging discourse markers for training new models

Code for "Causal autoregressive flows" - AISTATS, 2021