A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

Overview

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Awesome Machine Learning Jupyter Notebooks for Google Colaboratory Awesome

A curated list of Machine Learning and Deep Learning tutorials in Jupyter Notebook format ready to run in Google Colaboratory

You can find the credits for the authors in the header of each Jupyter Notebook.

Contents

Machine Learning

Deep Learning

Reinforcement Learning

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License

CC0

Owner
Carlos Toxtli
PhD, HCI Research Scientist & Engineer, Data Scientist, Full Stack Software Engineer, CTO, Bot Developer, Freelancer, Dad
Carlos Toxtli
The official pytorch implementation of our paper "Is Space-Time Attention All You Need for Video Understanding?"

TimeSformer This is an official pytorch implementation of Is Space-Time Attention All You Need for Video Understanding?. In this repository, we provid

Facebook Research 1k Dec 31, 2022
Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy.

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usa

Marcel R. 349 Aug 06, 2022
Official PyTorch implementation of the paper: DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample

DeepSIM: Image Shape Manipulation from a Single Augmented Training Sample (ICCV 2021 Oral) Project | Paper Official PyTorch implementation of the pape

Eliahu Horwitz 393 Dec 22, 2022
Pytorch library for end-to-end transformer models training and serving

Pytorch library for end-to-end transformer models training and serving

Mikhail Grankin 768 Jan 01, 2023
Image augmentation library in Python for machine learning.

Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independe

Marcus D. Bloice 4.8k Jan 07, 2023
Diagnostic tests for linguistic capacities in language models

LM diagnostics This repository contains the diagnostic datasets and experimental code for What BERT is not: Lessons from a new suite of psycholinguist

61 Jan 02, 2023
A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Graph Wavelet Neural Network ⠀⠀ A PyTorch implementation of Graph Wavelet Neural Network (ICLR 2019). Abstract We present graph wavelet neural network

Benedek Rozemberczki 490 Dec 16, 2022
Code for paper "Vocabulary Learning via Optimal Transport for Neural Machine Translation"

**Codebase and data are uploaded in progress. ** VOLT(-py) is a vocabulary learning codebase that allows researchers and developers to automaticaly ge

416 Jan 09, 2023
Tensorflow 2 implementations of the C-SimCLR and C-BYOL self-supervised visual representation methods from "Compressive Visual Representations" (NeurIPS 2021)

Compressive Visual Representations This repository contains the source code for our paper, Compressive Visual Representations. We developed informatio

Google Research 30 Nov 23, 2022
MPI-IS Mesh Processing Library

Perceiving Systems Mesh Package This package contains core functions for manipulating meshes and visualizing them. It requires Python 3.5+ and is supp

Max Planck Institute for Intelligent Systems 494 Jan 06, 2023
Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Start-to-finish tutorial for interactive music co-creation in PyTorch and Tensorflow.js

Chris Donahue 98 Dec 14, 2022
An implementation for `Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction`

Text2Event An implementation for Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction Please contact Yaojie Lu (@

Roger 153 Jan 07, 2023
Keras Model Implementation Walkthrough

Keras Model Implementation Walkthrough

Luke Wood 17 Sep 27, 2022
DecoupledNet is semantic segmentation system which using heterogeneous annotations

DecoupledNet: Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation Created by Seunghoon Hong, Hyeonwoo Noh and Bohyung Han at POSTE

Hyeonwoo Noh 74 Sep 22, 2021
More than a hundred strange attractors

dysts Analyze more than a hundred chaotic systems. Basic Usage Import a model and run a simulation with default initial conditions and parameter value

William Gilpin 185 Dec 23, 2022
Post-training Quantization for Neural Networks with Provable Guarantees

Post-training Quantization for Neural Networks with Provable Guarantees Authors: Jinjie Zhang ( Yixuan Zhou 2 Nov 29, 2022

Official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

Vision Transformer with Progressive Sampling This is the official implementation of the paper Vision Transformer with Progressive Sampling, ICCV 2021.

yuexy 123 Jan 01, 2023
A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN

A Pytorch Implementation of Source Data-free Domain Adaptation for a Faster R-CNN Please follow Faster R-CNN and DAF to complete the environment confi

2 Jan 12, 2022
Code to reproduce the experiments in the paper "Transformer Based Multi-Source Domain Adaptation" (EMNLP 2020)

Transformer Based Multi-Source Domain Adaptation Dustin Wright and Isabelle Augenstein To appear in EMNLP 2020. Read the preprint: https://arxiv.org/a

CopeNLU 36 Dec 05, 2022
Source code for models described in the paper "AudioCLIP: Extending CLIP to Image, Text and Audio" (https://arxiv.org/abs/2106.13043)

AudioCLIP Extending CLIP to Image, Text and Audio This repository contains implementation of the models described in the paper arXiv:2106.13043. This

458 Jan 02, 2023