Tag

deep

Machine Learning as a Pipeline

From Deep Learning Patterns and Practices by Andrew Ferlitsch

Like the best software engineering, modern deep learning uses a pipeline architecture based on reusable patterns.

When Machine Learning Becomes Machine Design: new paradigms and patterns for automated deep learning

Andrew Ferlitsch reveals new paradigms–and patterns–for automated deep learning

Andrew Ferlitsch, from the developer relations team at Google Cloud AI, is so far out on the cutting edge of machine learning and artificial intelligence that he has to invent new terminology to describe what’s happening in Cloud AI with Google Cloud’s enterprise clients. In this interview with editors at Manning Publications, he talks about the current and coming changes in machine learning systems, starting with the concept of model amalgamation. Ferlitsch is currently writing a book, Deep Learning Design Patterns, which collects his ideas along with the most important composable model components.

Read more author interviews here

Understanding the Math Behind the Algorithms

From Math and Architectures of Deep Learning by Krishnendu Chaudhury

Store, Analyse, and Filter Data Better

From Pandas in Action by Boris Paskhaver

A Pre-Trained Model that Fakes It until It Makes It

From Deep Learning with PyTorch by Eli Stevens and Luca Antiga

This article explores the use and capabilities of GANs, using a fictional (and inadvasible) example of aspiring art forgers.

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