Open Source Virtual Background

With many of us around the globe under shelter in place due to COVID-19 video calls have become a lot more common. In particular, ZOOM has controversially become very popular. Arguably Zoom’s most interesting feature is the “Virtual Background” support which allows users to replace the background behind them in their webcam video feed with any image (or video)…

https://elder.dev/posts/open-source-virtual-background/

Introducing TensorFlow.js: Machine Learning in Javascript

We’re excited to introduce TensorFlow.js, an open-source library you can use to define, train, and run machine learning models entirely in the browser, using Javascript and a high-level layers API. If you’re a Javascript developer who’s new to ML, TensorFlow.js is a great way to begin learning. Or, if you’re a ML developer who’s new to Javascript, read on to learn more about new opportunities for in-browser ML. In this post, we’ll give you a quick overview of TensorFlow.js, and getting started resources you can use to try it out.

https://medium.com/tensorflow/introducing-tensorflow-js-machine-learning-in-javascript-bf3eab376db

Industrial-Strength Natural Language Processing

spaCy excels at large-scale information extraction tasks. It’s written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.

https://spacy.io/

Introduction to TensorFlow Datasets and Estimators

Datasets and Estimators are two key TensorFlow features you should use:

  • Datasets: The best practice way of creating input pipelines (that is, reading data into your program).
  • Estimators: A high-level way to create TensorFlow models. Estimators include pre-made models for common machine learning tasks, but you can also use them to create your own custom models.

https://developers.googleblog.com/2017/09/introducing-tensorflow-datasets.html

https://developers.googleblog.com/2017/11/introducing-tensorflow-feature-columns.html

An Introduction to Implementing Neural Networks using TensorFlow

Introduction

If you have been following Data Science / Machine Learning, you just can’t miss the buzz around Deep Learning and Neural Networks. Organizations are looking for people with Deep Learning skills wherever they can. From running competitions to open sourcing projects and paying big bonuses, people are trying every possible thing to tap into this limited pool of talent. Self driving engineers are being hunted by the big guns in automobile industry, as the industry stands on the brink of biggest disruption it faced in last few decades!

If you are excited by the prospects deep learning has to offer, but have not started your journey yet – I am here to enable it. Starting with this article, I will write a series of articles on deep learning covering the popular Deep Learning libraries and their hands-on implementation.

In this article, I will introduce TensorFlow to you. After reading this article you will be able to understand application of neural networks and use TensorFlow to solve a real life problem. This article will require you to know the basics of neural networks and have familiarity with programming. Although the code in this article is in python, I have focused on the concepts and stayed as language-agnostic as possible.

Let’s get started!

TensorFlow

https://www.analyticsvidhya.com/blog/2016/10/an-introduction-to-implementing-neural-networks-using-tensorflow/

Using the TensorFlow API: An Introductory Tutorial Series

This post summarizes and links to a great multi-part tutorial series on learning the TensorFlow API for building a variety of neural networks, as well as a bonus tutorial on backpropagation from the beginning.


By Erik Hallström, Deep Learning Research Engineer.

Editor’s note: The TensorFlow API has undergone changes since this series was first published. However, the general ideas are the same, and an otherwise well-structured tutorial such as this provides a great jumping off point and opportunity to consult the API documentation to identify and implement said changes.

Schematic of RNN processing sequential over time
Schematic of a RNN processing sequential data over time.

https://www.kdnuggets.com/2017/06/using-tensorflow-api-tutorial-series.html