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tensorbag - Collection of tensorflow notebooks tutorials for implementing the most important Deep Learning algorithms

  •    Jupyter

Tensorbag is a collection of tensorflow tutorial on different Deep Learning and Machine Learning algorithms. The tutorials are organised as jupyter notebooks and require tensorflow >= 1.5. There is a subset of notebooks identified with the tag [quiz] that directly ask to the reader to complete part of the code. In the same folder there is always a complementary notebook with the complete solution.

Deep-Learning-From-Scratch - Six snippets of code that made deep learning what it is today.

  •    Jupyter

There are six snippets of code that made deep learning what it is today. Coding the History of Deep Learning on Floydhub' s blog covers the inventors and the background to their breakthroughs. In this repo, you can find all the code samples from the story.

tf-vqvae - Tensorflow Implementation of the paper [Neural Discrete Representation Learning](https://arxiv

  •    Jupyter

This repository implements the paper, Neural Discrete Representation Learning (VQ-VAE) in Tensorflow. ⚠️ This is not an official implementation, and might have some glitch (,or a major defect).




MNIST-adversarial-images - Create adversarial images to fool a MNIST classifier in TensorFlow

  •    Jupyter

The original concept of this notebook was based on a Machine Learning (intern) candidate tech challenge from the Toronto startup 500px. When I first saw the posting, it was at the beginning of my 3 month career pivot into Deep Learning and I thought this challenge would be a great way for me to benchmark my progress once I get started. You can read more about my career transition journey on Medium and a revised/updated version on LinkedIn. Although, I didn't follow through with providing the entire final output of the challenge, I'm quite satisfied that I've successfully completed it and consider it a demonstration of my current knowledge and capability. Prior to starting this challenge, I completed Fast.ai: Practical Deep Learning - Part 1. Read through my blog post to see my reading material - Deep Learning Reading List.