Machine learning notebooks in different subjects optimized to run in google collaboratory

Overview

Notebooks

Name Description Category Link
Training pix2pix This notebook shows a simple pipeline for training pix2pix on a simple dataset. Most of the code is based on this implementation. GAN
One Place This notebook shows how to train, test then deploy models in the browser directly from one notebook. We use a simple XOR example to prove this simple concept. Deployment
TPU vs GPU Google recently allowed training on TPUs for free on colab. This notebook explains how to enable TPU training. Also, it reports some benchmarks using mnist dataset by comparing TPU and GPU performance. TPU
Keras Custom Data Generator This notebook shows to create a custom data genertor in keras. Data Generatation
Eager Execution (1) As we know that TenosrFlow works with static graphs. So, first you have to create the graph then execute it later. This makes debugging a bit complicated. With Eager Execution you can now evalute operations directly without creating a session. Dynamic Graphs
Eager Execution (2) In this notebook I explain different concepts in eager execution. I go over variables, ops, gradients, custom gradients, callbacks, metrics and creating models with tf.keras and saving/restoring them. Dynamic Graphs
Sketcher Create a simple app to recognize 100 drawings from the quickdraw dataset. A simple CNN model is created and served to deoploy in the browser to create a sketch recognizer app. Deployment
QuickDraw10 In this notebook we provide QuickDraw10 as an alternative for MNIST. A script is provided to download and load a preprocessed dataset for 10 classes with training and testing split. Also, a simple CNN model is implemented for training and testing. Data Preperation
Autoencoders Autoencoders consists of two structures: the encoder and the decoder. The encoder network downsamples the data into lower dimensions and the decoder network reconstructs the original data from the lower dimension representation. The lower dimension representation is usually called latent space representation. Auto-encoder
Weight Transfer In this tutorial we explain how to transfer weights from a static graph model built with TensorFlow to a dynamic graph built with Keras. We will first train a model using Tensorflow then we will create the same model in keras and transfer the trained weights between the two models. Weights Save and Load
BigGan (1) Create some cool gifs by interpolation in the latent space of the BigGan model. The model is imported from tensorflow hub. GAN
BigGan (2) In this notebook I give a basic introduction to bigGans. I also, how to interpolate between z-vector values. Moreover, I show the results of multiple experiments I made in the latent space of BigGans. GAN
Mask R-CNN In this notebook a pretrained Mask R-CNN model is used to predict the bounding box and the segmentation mask of objects. I used this notebook to create the dataset for training the pix2pix model. Segmentation
QuickDraw Strokes A notebook exploring the drawing data of quickdraw. I also illustrate how to make a cool animation of the drawing process in colab. Data Preperation
U-Net The U-Net model is a simple fully convolutional neural network that is used for binary segmentation i.e foreground and background pixel-wise classification. In this notebook we use it to segment cats and dogs from arbitrary images. Segmentation
Localizer A simple CNN with a regression branch to predict bounding box parameters. The model is trained on a dataset of dogs and cats with bounding box annotations around the head of the pets. Object Localization
Classification and Localization We create a simple CNN with two branches for classification and locazliation of cats and dogs. Classification, Localization
Transfer Learning A notebook about using Mobilenet for transfer learning in TensorFlow. The model is very fast and achieves 97% validation accuracy on a binary classification dataset. Transfer Learning
Hand Detection In this task we want to localize the right and left hands for each person that exists in a single frame. It acheives around 0.85 IoU. Detection
Face Detection In this task we used a simple version of SSD for face detection. The model was trained on less than 3K images using TensorFlow with eager execution Detection
TensorFlow 2.0 In this task we use the brand new TF 2.0 with default eager execution. We explore, tensors, gradients, dataset and many more. Platform
SC-FEGAN In this notebook, you can play directly with the SC-FEGAN for face-editting directly in the browser. GAN
Swift for TensorFlow Swift for TensorFlow is a next-generation platform for machine learning that incorporates differentiable programming. In this notebook a go over its basics and also how to create a simple NN and CNN. Platform
GCN Ever asked yourself how to use convolution networks for non Euclidean data for instance graphs ? GCNs are becoming increasingly popular to solve such problems. I used Deep GCNs to classify spammers & non-spammers. Platform
Owner
Zaid Alyafeai
PhD student
Zaid Alyafeai
BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation

BEAMetrics: Benchmark to Evaluate Automatic Metrics in Natural Language Generation Installing The Dependencies $ conda create --name beametrics python

7 Jul 04, 2022
Deploy a ML inference service on a budget in less than 10 lines of code.

BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end.

1.3k Dec 25, 2022
Computational inteligence project on faces in the wild dataset

Table of Contents The general idea How these scripts work? Loading data Needed modules and global variables Parsing the arrays in dataset Extracting a

tooraj taraz 4 Oct 21, 2022
HomoInterpGAN - Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation

HomoInterpGAN Homomorphic Latent Space Interpolation for Unpaired Image-to-image Translation (CVPR 2019, oral) Installation The implementation is base

Ying-Cong Chen 99 Nov 15, 2022
This is the official implementation for the paper "Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization" in NeurIPS 2021.

MPMAB_BEACON This is code used for the paper "Decentralized Multi-player Multi-armed Bandits: Beyond Linear Reward Functions", Neurips 2021. Requireme

Cong Shen Research Group 0 Oct 26, 2021
A PyTorch-based library for semi-supervised learning

News If you want to join TorchSSL team, please e-mail Yidong Wang ([email protected]<

1k Jan 06, 2023
KGDet: Keypoint-Guided Fashion Detection (AAAI 2021)

KGDet: Keypoint-Guided Fashion Detection (AAAI 2021) This is an official implementation of the AAAI-2021 paper "KGDet: Keypoint-Guided Fashion Detecti

Qian Shenhan 35 Dec 29, 2022
A decent AI that solves daily Wordle puzzles. Works with different websites with similar wordlists,.

Wordle-AI A decent AI that solves daily "Wordle" puzzles. Works with different websites with similar wordlists. When prompted with "Word:" enter the w

Ethan 1 Feb 10, 2022
High-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

TL;DR Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently. Click on the image to

4.2k Jan 01, 2023
Official implementation of the paper Chunked Autoregressive GAN for Conditional Waveform Synthesis

PyEmits, a python package for easy manipulation in time-series data. Time-series data is very common in real life. Engineering FSI industry (Financial

Descript 150 Dec 06, 2022
The official code of "SCROLLS: Standardized CompaRison Over Long Language Sequences".

SCROLLS This repository contains the official code of the paper: "SCROLLS: Standardized CompaRison Over Long Language Sequences". Links Official Websi

TAU NLP Group 39 Dec 23, 2022
This repository contains the source code and data for reproducing results of Deep Continuous Clustering paper

Deep Continuous Clustering Introduction This is a Pytorch implementation of the DCC algorithms presented in the following paper (paper): Sohil Atul Sh

Sohil Shah 197 Nov 29, 2022
FasterAI: A library to make smaller and faster models with FastAI.

Fasterai fasterai is a library created to make neural network smaller and faster. It essentially relies on common compression techniques for networks

Nathan Hubens 193 Jan 01, 2023
Guiding evolutionary strategies by (inaccurate) differentiable robot simulators @ NeurIPS, 4th Robot Learning Workshop

Guiding Evolutionary Strategies by Differentiable Robot Simulators In recent years, Evolutionary Strategies were actively explored in robotic tasks fo

Vladislav Kurenkov 4 Dec 14, 2021
A general framework for deep learning experiments under PyTorch based on pytorch-lightning

torchx Torchx is a general framework for deep learning experiments under PyTorch based on pytorch-lightning. TODO list gan-like training wrapper text

Yingtian Liu 6 Mar 17, 2022
A modular active learning framework for Python

Modular Active Learning framework for Python3 Page contents Introduction Active learning from bird's-eye view modAL in action From zero to one in a fe

modAL 1.9k Dec 31, 2022
Code for our NeurIPS 2021 paper 'Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation'

Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation (NeurIPS 2021) Code for our NeurIPS 2021 paper 'Exploiting the Intri

Shiqi Yang 53 Dec 25, 2022
Official implementation of the paper DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows

DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional Flows Official implementation of the paper DeFlow: Learning Complex Im

Valentin Wolf 86 Nov 16, 2022
Linescanning - Package for (pre)processing of anatomical and (linescanning) fMRI data

line scanning repository This repository contains all of the tools used during the acquisition and postprocessing of line scanning data at the Spinoza

Jurjen Heij 4 Sep 14, 2022
GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

If you are using this code in your own project, please cite our paper: @inproceedings{awiszus2020toadgan, title={TOAD-GAN: Coherent Style Level Gene

Maren A. 13 Dec 14, 2022