TensorFlow Weekly Issue 4

TensorFlow Weekly Issue 4 — August 14, 2018

Hi there, these are the top TensorFlow links from my weekly curation. If you're having trouble viewing this email, please read the online version.

Learn TensorFlow, the Word2Vec model, and the TSNE algorithm using rock bands — Learning the “TensorFlow way” to build a neural network can seem like a big hurdle to getting started with machine learning. In this tutorial, we’ll take it step by step and explain all of the critical components involved as we build a Bands2Vec model using Pitchfork data from Kaggle...
Patrick Ferris

Comparing Different Methods of Achieving Sparse Coding in Tensorflow [ Manual Back Prop in TF ] — have been studying more about sparse coding and different ways to achieve it, and today I wanted to compare some of them. And below are all the cases where I am going to compare one another: Case a: Pure Auto Encoders, Case b: Auto Encoders with L2 Regularization, Case c: Sparse Auto Encoders from Andrew NG’s Course, Case d: Simple, Efficient, and Neural Algorithms for Sparse Coding, and Case e: k-Sparse Autoencoders...
Jae Duk Seo

Code with Eager Execution, Run with Graphs: Optimizing Your Code with RevNet as an Example — Eager execution simplifies the model building experience in TensorFlow, whereas graph execution can provide optimizations that make models run faster with better memory efficiency. This blog post showcases how to write TensorFlow code so that models built using eager execution with the tf.keras API can be converted to graphs and eventually deployed on Cloud TPUs with the support of the tf.estimator API...
Xuechen Li

Building a text classification model with TensorFlow Hub and Estimators — We often see transfer learning applied to computer vision models, but what about using it for text classification? Enter TensorFlow Hub, a library for enhancing your TF models with transfer learning. Transfer learning is the process of taking the weights and variables of a pre-existing model that has already been trained on lots of data and leveraging it for your own data and prediction task...
Sara Robinson

Implementing a neural Part-of-Speech tagger — DyNet, PyTorch and Tensorflow are complex frameworks with different ways of approaching neural network implementation and variations in default behaviour. This page is intended to show how to implement the same non-trivial model in all three. The design of the page is motivated by my own preference for a complete program with annotations, rather than the more common tutorial style of introducing code piecemeal in between discussion. The design of the code is also geared towards providing a complete picture of how things fit together. For a non-tutorial version of this code it would be better to use abstraction to improve flexibility, but that would have complicated the flow here...
Jonathan K. Kummerfeld

Colorizing B&W Photos with Neural Networks — Earlier this year, Amir Avni used neural networks to troll the subreddit /r/Colorization - a community where people colorize historical black and white images manually using Photoshop. They were astonished with Amir’s deep learning bot - what could take up to a month of manual labour could now be done in just a few seconds...I was fascinated by Amir’s neural network, so I reproduced it and documented the process. First off, let’s look at some of the results/failures from my experiments (scroll to the bottom for the final result)....
Emil Wallner

HPJS: Hyperparameter Optimization for Javascript — Google’s TensorflowJS is exciting because of multiple advantages it has over the traditional Tensorflow Python (being able to run models in the browser, for example). But due to it being so new, there are far fewer accompanying libraries for Javascript than there are for Python. Because of this, my dad and I are releasing HyperparametersJS (hpjs), a Javascript library for hyperparameter optimization...
Atanas Stoyanov & Martin Stoyanov
 

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Cheers,
Sebastian