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

Deep Learningmachine-learningdeep-learningkerasvggdcganautoencoderdensenetresnetkeras-tutorialssqueezenetinceptionresnextautomlmobilenetsiamese-networkshufflenetsenetxceptiontensorflow-keras
Overview

License License

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.

Owner
Google Cloud Platform
Google Cloud Platform
GitHub Repository
The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"

Swin-Unet The codes for the work "Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation"(https://arxiv.org/abs/2105.05537). A validatio

869 Jan 07, 2023
Papers about explainability of GNNs

Papers about explainability of GNNs

Dongsheng Luo 236 Jan 04, 2023
[SIGGRAPH 2020] Attribute2Font: Creating Fonts You Want From Attributes

Attr2Font Introduction This is the official PyTorch implementation of the Attribute2Font: Creating Fonts You Want From Attributes. Paper: arXiv | Rese

Yue Gao 200 Dec 15, 2022
Code for "Typilus: Neural Type Hints" PLDI 2020

Typilus A deep learning algorithm for predicting types in Python. Please find a preprint here. This repository contains its implementation (src/) and

47 Nov 08, 2022
Source code for our EMNLP'21 paper 《Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning》

Child-Tuning Source code for EMNLP 2021 Long paper: Raise a Child in Large Language Model: Towards Effective and Generalizable Fine-tuning. 1. Environ

46 Dec 12, 2022
When are Iterative GPs Numerically Accurate?

When are Iterative GPs Numerically Accurate? This is a code repository for the paper "When are Iterative GPs Numerically Accurate?" by Wesley Maddox,

Wesley Maddox 1 Jan 06, 2022
An implementation for Neural Architecture Search with Random Labels (CVPR 2021 poster) on Pytorch.

Neural Architecture Search with Random Labels(RLNAS) Introduction This project provides an implementation for Neural Architecture Search with Random L

18 Nov 08, 2022
Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Degree-Quant This repo provides a clean re-implementation of the code associated with the paper Degree-Quant: Quantization-Aware Training for Graph Ne

35 Oct 07, 2022
Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking

Probabilistic Tracklet Scoring and Inpainting for Multiple Object Tracking (CVPR 2021) Pytorch implementation of the ArTIST motion model. In this repo

Fatemeh 38 Dec 12, 2022
Implementation for paper: Self-Regulation for Semantic Segmentation

Self-Regulation for Semantic Segmentation This is the PyTorch implementation for paper Self-Regulation for Semantic Segmentation, ICCV 2021. Citing SR

Dong ZHANG 30 Nov 21, 2022
NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go

NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go This repository provides our implementation of the CVPR 2021 paper NeuroMorp

Meta Research 35 Dec 08, 2022
LaneDetectionAndLaneKeeping - Lane Detection And Lane Keeping

LaneDetectionAndLaneKeeping This project is part of my bachelor's thesis. The go

5 Jun 27, 2022
PyTorch code accompanying the paper "Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning" (NeurIPS 2021).

HIGL This is a PyTorch implementation for our paper: Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning (NeurIPS 2021). Our cod

Junsu Kim 20 Dec 14, 2022
An image classification app boilerplate to serve your deep learning models asap!

Image 🖼 Classification App Boilerplate Have you been puzzled by tons of videos, blogs and other resources on the internet and don't know where and ho

Smaranjit Ghose 27 Oct 06, 2022
Implementation of "RaScaNet: Learning Tiny Models by Raster-Scanning Image" from CVPR 2021.

RaScaNet: Learning Tiny Models by Raster-Scanning Images Deploying deep convolutional neural networks on ultra-low power systems is challenging, becau

SAIT (Samsung Advanced Institute of Technology) 5 Dec 26, 2022
The most simple and minimalistic navigation dashboard.

Navigation This project follows a goal to have simple and lightweight dashboard with different links. I use it to have my own self-hosted service dash

Yaroslav 23 Dec 23, 2022
Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

Shortformer This repository contains the code and the final checkpoint of the Shortformer model. This file explains how to run our experiments on the

Ofir Press 138 Apr 15, 2022
Grounding Representation Similarity with Statistical Testing

Grounding Representation Similarity with Statistical Testing This repo contains code to replicate the results in our paper, which evaluates representa

26 Dec 02, 2022
A small demonstration of using WebDataset with ImageNet and PyTorch Lightning

A small demonstration of using WebDataset with ImageNet and PyTorch Lightning This is a small repo illustrating how to use WebDataset on ImageNet. usi

50 Dec 16, 2022
This is a simple framework to make object detection dataset very quickly

FastAnnotation Table of contents General info Requirements Setup General info This is a simple framework to make object detection dataset very quickly

Serena Tetart 1 Jan 24, 2022