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https://github.com/zaidalyafeai/Notebooks

Machine learning notebooks in different subjects optimized to run in google collaboratory
https://github.com/zaidalyafeai/Notebooks

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

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# 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