A Deep Learning System from an Engineer’s Perspective

From Engineering Deep Learning Systems by Chi Wang and Donald Szeto

This article presents what prospective readers can expect to learn from this book and why you should learn it.

Read it if you’re a software developer interested in transitioning your skills to the field of deep learning system design or an engineering-minded data scientist who want to build more effective delivery pipelines.

Converting Pure Deep Learning with PyTorch to Use Lightning and Hangar

In this video, machine learning expert Eli Stevens showcases how to use open-source libraries that are available in the PyTorch ecosystem to cut down the amount of the code that you want to write.

Applying VACUUM to Data

From Cloud Native Machine Learning by Carl Osipov

The goal of this article is to teach you the data quality criteria you should use across any machine learning project, regardless of the dataset. This means that this part deals primarily with concepts rather than code.

Automatic Differentiation in Python and PyTorch

Deep dive with Carl Osipov into understanding automatic differentiation used by PyTorch autograd for deep learning

Leverage Cloud-Based Machine Learning Services

From Serverless Machine Learning in Action by Carl Osipov

Understanding the Math Behind the Algorithms

From Math and Architectures of Deep Learning by Krishnendu Chaudhury

Active Transfer Learning with PyTorch

By Robert Munro, author of Human-in-the-Loop Machine Learning

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.

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.

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