From Probabilistic Deep Learning with Python by Oliver Dürr, Beate Sick, and Elvis Murina
This article discusses using deep learning for data that act like images.
From Deep Learning and the Game of Go by Max Pumperla and Kevin Ferguson
This article shows you how to use the minimax algorithm to help your game bot decide its next move.
From Math for Programmers by Paul Orland
Six questions 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.
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.
From Deep Learning with PyTorch by Eli Stevens and Luca Antiga
In this article, we explore some of PyTorch’s capabilities by playing with pre-trained networks.
From GANs in Action
By Jakub Langr and Vladimir Bok
From Deep Learning with PyTorch by Eli Stevens and Luca Antiga
This article introduces you to PyTorch and discusses why you might want to use it in your deep learning projects.
