TensorFlow Weekly Issue 2

TensorFlow Weekly Issue 2 — July 31, 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.

Deep Bayesian Bandits Library — This library corresponds to the Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling paper, published in ICLR 2018. We provide a benchmark to test decision-making algorithms for contextual-bandits. In particular, the current library implements a variety of algorithms (many of them based on approximate Bayesian Neural Networks and Thompson sampling), and a number of real and syntethic data problems exhibiting a diverse set of properties...
Carlos Riquelme, George Tucker, Jasper Snoek

OSCON 2018 — TensorFlow Day — Last week I went to Portland to attend OSCON¹ (Open Source Convention) for the first time. This was a special edition since the event was celebrating its 20th Anniversary...Tuesday, July 17th, was TensorFlow Day. The day was filled with great talks including speakers from the community, showcasing open source machine learning with TensorFlow...
Gabriela de Queiroz

How to get started debugging TensorFlow — Debugging in general can be a tedious and challenging task. Nevertheless, you must be comfortable going through the written code and identifying problems. Normally there are many guides, and the process of debugging is often well documented for many languages and frameworks...When it comes to TensorFlow, however, some new challenges arise because of the way...A TensorFlow Core program consists of two discrete sections: a) Building the computational graph (a tf.Graph), and b) Running the computational graph (using a tf.Session)...
Daniel Deutsch

Understanding Tensorflow's tensors shape: static and dynamic — Describing computational graphs is just a matter of connecting nodes correctly. Connecting nodes seems a trivial operation, but it hides some difficulties related to the shape of tensors. This article will guide you through the concept of tensor’s shape in both its variants: static and dynamic...
Paolo Galeone

Classifying images using Keras MobileNet in Google Chrome — In this blog post, we will understand how to perform image classification using Keras MobileNet, deploy it in Google Chrome using TensorFlow.js and use it to make live predictions in the browser...
Gogul Ilango

An Advanced Example of Tensorflow Estimators Part (1/3) — Estimators were introduced in version 1.3 of the Tensorflow API, and are used to abstract and simplify training, evaluation and prediction. If you haven’t worked with Estimators before I suggest to start by reading this article and get some familiarity as I won’t be covering all of the basics when using estimators. In no means do I think this article should be seen as best practice but I hope it will demystify and clarify some aspects of using Estimators...
Tijmen Verhulsdonck

Client-side prediction with TensorFlow.js — Today I will try to share my knowledge and show how to deploy a model in the way that some of the calculations will be done by the client side. The following post is meant for everyone who created a model and wants to reduce a load of the server by delegating predict part to a client. Especially for Data Scientists who use Python on a daily basis and have a little knowledge of JavaScript...
Matt Kovtun

Export and Import Models with Tensorflow SavedModelBuilder: an LSTM Example — While the official docs favor SavedModelBuilder over checkpoint tf.train.Saver and tf.saved_model.simple_save, detailed explanations and examples are sparse...My project is to translate Russian into IPA (International Phonetic Alphabet) which is basically an RNN application using sequence-to-sequence (seq2seq) LSTM. I was able to cleanly export a model, import it, and use it in code...
Zhanwen Chen

Training a Tensorflow Image Classification Model and Integrating It Into ios Apps Using Core ML — If you are reading this article, you are probably trying to build an iOS app that can recognize what it sees. By the time this article is released, there are different ways you can make your app classify images. One of them is using Create ML that is announced at WWDC18 by Apple. This is the easiest way for iOS developers although it is only compatible with iOS 12 devices. For the older versions of iOS, one way I’ve recently used and strongly suggest is retraining a TensorFlow model called MobileNet and converting it into Core ML. You can also use the retrained model within your Android Apps...
Nev Acar

Detecting Malicious Requests with Keras & Tensorflow — What if you could use the power of Google’s Tensorflow engine to decide on whether a given request is considered malicious? Well that was the question I was looking to answer while participating in Slalom’s recent AI hackathon. The following post outlines the technical details of a PoC for a security monitoring application which was built with the help of a couple other Slalomites...
Adam Kusey

Using ML to predict insulin use for Type 1 Diabetes — TensorFlow is being used in many different industries, including healthcare. In this episode of TensorFlow Meets, Software Engineer Pete Warden sits down with Marius Eriksen to discuss how he has used TensorFlow to help his daughter better manage type 1 diabetes. Watch to learn more and see the device he engineered to be controlled by a Raspberry Pi...
Pete Warden and Marius Eriksen
 

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Cheers,
Sebastian