Subject

Data

What Happens behind the Scenes with Spark

From Spark with Java by Jean Georges Perrin

You’ve probably seen a simple use-case where Spark ingests data from a CSV file, then performs a simple operation, and then stores the result in the database. In this article, you’re going to see what happened behind the scenes.

Six Questions with Andrew Trask

Privacy, Twitter, and Machine Learning

Andrew Trask, author of Grokking Deep Learning

By Frances Lefkowitz, Manning Development Editor

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.

What do Cooking Pasta and Data Science Have in Common?

From Data Science at Scale with Python and Dask by Jesse C. Daniel

This article discusses Dask, how it compares to Apache Spark, and how to create and understand directed acyclic graphs using the example of the delicious Italian pasta dish bucatini all’Amatriciana.

PyTorch Crash Course, Part 2

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.

A Practice-Oriented Approach to Data Science


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

Get into Deep Learning


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From Grokking Deep Learning in Motion
By Beau Carnes

Learn to Create Constantly-Learning AI Agents


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From Reinforcement Learning in Motion
By Phil Tabor

Deep Learning with Generative Adversarial Networks


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From GANs in Action
By Jakub Langr and Vladimir Bok

PyTorch Crash Course, Part 1

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.

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