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
Show-attend-and-tell - TensorFlow Implementation of "Show, Attend and Tell"

Show, Attend and Tell Update (December 2, 2016) TensorFlow implementation of Show, Attend and Tell: Neural Image Caption Generation with Visual Attent

Yunjey Choi 902 Nov 29, 2022
Chunkmogrify: Real image inversion via Segments

Chunkmogrify: Real image inversion via Segments Teaser video with live editing sessions can be found here This code demonstrates the ideas discussed i

David Futschik 112 Jan 04, 2023
Model that predicts the probability of a Twitter user being anti-vaccination.

stylebody {text-align: justify}/style AVAXTAR: Anti-VAXx Tweet AnalyzeR AVAXTAR is a python package to identify anti-vaccine users on twitter. The

10 Sep 27, 2022
Code base for "On-the-Fly Test-time Adaptation for Medical Image Segmentation"

On-the-Fly Adaptation Official Pytorch Code base for On-the-Fly Test-time Adaptation for Medical Image Segmentation Paper Introduction One major probl

Jeya Maria Jose 17 Nov 10, 2022
A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022)

A Text Attention Network for Spatial Deformation Robust Scene Text Image Super-resolution (CVPR2022) https://arxiv.org/abs/2203.09388 Jianqi Ma, Zheto

MA Jianqi, shiki 104 Jan 05, 2023
Auto-Lama combines object detection and image inpainting to automate object removals

Auto-Lama Auto-Lama combines object detection and image inpainting to automate object removals. It is build on top of DE:TR from Facebook Research and

44 Dec 09, 2022
RGB-D Local Implicit Function for Depth Completion of Transparent Objects

RGB-D Local Implicit Function for Depth Completion of Transparent Objects [Project Page] [Paper] Overview This repository maintains the official imple

NVIDIA Research Projects 43 Dec 12, 2022
This repository is to support contributions for tools for the Project CodeNet dataset hosted in DAX

The goal of Project CodeNet is to provide the AI-for-Code research community with a large scale, diverse, and high quality curated dataset to drive innovation in AI techniques.

International Business Machines 1.2k Jan 04, 2023
Official code of the paper "ReDet: A Rotation-equivariant Detector for Aerial Object Detection" (CVPR 2021)

ReDet: A Rotation-equivariant Detector for Aerial Object Detection ReDet: A Rotation-equivariant Detector for Aerial Object Detection (CVPR2021), Jiam

csuhan 334 Dec 23, 2022
PyTorch Implementation of SSTNs for hyperspectral image classifications from the IEEE T-GRS paper "Spectral-Spatial Transformer Network for Hyperspectral Image Classification: A FAS Framework."

PyTorch Implementation of SSTN for Hyperspectral Image Classification Paper links: SSTN published on IEEE T-GRS. Also, you can directly find the imple

Zilong Zhong 54 Dec 19, 2022
Implementation of Bidirectional Recurrent Independent Mechanisms (Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neural Networks with Attention over Modules)

BRIMs Bidirectional Recurrent Independent Mechanisms Implementation of the paper Learning to Combine Top-Down and Bottom-Up Signals in Recurrent Neura

Sarthak Mittal 26 May 26, 2022
Books, Presentations, Workshops, Notebook Labs, and Model Zoo for Software Engineers and Data Scientists wanting to learn the TF.Keras Machine Learning framework

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

Google Cloud Platform 792 Dec 28, 2022
It helps user to learn Pick-up lines and share if he has a better one

Pick-up-Lines-Generator(Open Source) It helps user to learn Pick-up lines Share and Add one or many to the DataBase Unique SQLite DataBase AI Undercon

knock_nott 0 May 04, 2022
A curated list of awesome projects and resources related fastai

A curated list of awesome projects and resources related fastai

Tanishq Abraham 138 Dec 22, 2022
Paddle-Skeleton-Based-Action-Recognition - DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN

Paddle-Skeleton-Action-Recognition DecoupleGCN-DropGraph, ASGCN, AGCN, STGCN. Yo

Chenxu Peng 3 Nov 02, 2022
CNN visualization tool in TensorFlow

tf_cnnvis A blog post describing the library: https://medium.com/@falaktheoptimist/want-to-look-inside-your-cnn-we-have-just-the-right-tool-for-you-ad

InFoCusp 778 Jan 02, 2023
Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS of first stage is 3.42 and second stage is 3.47.

SDDNet Coarse implement of the paper "A Simultaneous Denoising and Dereverberation Framework with Target Decoupling", On DNS-2020 dataset, the DNSMOS

Cyril Lv 43 Nov 21, 2022
You Only 👀 One Sequence

You Only 👀 One Sequence TL;DR: We study the transferability of the vanilla ViT pre-trained on mid-sized ImageNet-1k to the more challenging COCO obje

Hust Visual Learning Team 666 Jan 03, 2023
Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Transfer Learning for Text Classification with Tensorflow Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01

DONGJUN LEE 82 Oct 22, 2022
Geometric Vector Perceptrons --- a rotation-equivariant GNN for learning from biomolecular structure

Geometric Vector Perceptron Implementation of equivariant GVP-GNNs as described in Learning from Protein Structure with Geometric Vector Perceptrons b

Dror Lab 142 Dec 29, 2022