Machine Learning

Deep Machine Learning

From Deep Learning Crash Course by Oliver Zeigermann

Develop Superior Machine Learning Algorithms

From Graph-Powered Machine Learning by Alessandro Negro


How Can You Benefit from Deep Learning?

From our Deep Learning Crash Course by Oliver Zeigermann


What Are GANs?

By Vladimir Bok, author of GANs in Action

This article discusses the history and meaning of Generative Adversarial Networks, and their potential.

What can Machine Learning do for your Business?


From Machine Learning for Business by Doug Hudgeon, and Richard Nichol

Privacy, Twitter, and Machine Learning: six questions with Andrew Trask

Privacy, Twitter, and Machine Learning

With Andrew Trask, author of Grokking Deep Learning

Andrew Trask is a researcher pursuing a Doctorate at Oxford University, where he focuses on Deep Learning with an emphasis on human language. He is also a leader at OpenMined.org, an open-source community of researchers and developers working on creating free and accessible tools for secure AI. Previously, Andrew was analytics product manager at Digital Reasoning, where he trained the world’s largest artificial neural network (with over 160 billion parameters) and helped guide the analytics for the Synthesys cognitive computing platform, which tackles problems in government intelligence, finance, and healthcare. Grokking Deep Learning is his first book.

Find Andrew online at his blog (iamtrask.github.io) and @iamtrask on Twitter.

PyTorch Crash Course, Part 3

From Deep Learning with PyTorch

by Eli Stevens and Luca Antiga

In this article, we explore some of PyTorch’s capabilities by playing generative adversarial networks.

S.A.M. Uses Machine Learning Services Hosted on AWS to Predict Crime

From AWS Machine Learning in Motion

By Kesha Williams

A Practice-Oriented Approach to Data Science


From Practical Data Science with R, Second Edition
By Nina Zumel and John Mount

Learn to Create Constantly-Learning AI Agents


From Reinforcement Learning in Motion
By Phil Tabor

© 2019 Manning — Design Credits