From Machine Learning Engineering in Action by Ben Wilson
Before we get into how successful planning phases for ML projects are undertaken, let’s go through a simulation of the genesis of a typical project at a company that doesn’t have an established or proven process for initiating ML work.
From Deep Learning for Natural Language Processing by Stephan Raaijmakers
This article covers multitask learning for NLP.
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.
From Transfer Learning in Action Dipanjan Sarkar and Raghav Bali
This article delves into tuning up a pre-trained ResNet-50 with one-cycle learning rate.
From Transfer Learning in Action Dipanjan Sarkar and Raghav Bali
This article discusses classifying images using transfer learning.
From PostGIS in Action, Third Edition by Leo S. Hsu and Regina O. Obe
In this article, you’ll learn what a topology is, how to build a topology from scratch, and how to use commonly available geometry data.
From Graph-Powered Machine Learning by Alessandro Negro
This article discusses managing data in graph-powered machine learning projects.
From Parallel and High-Performance Computing by Robert Robey and Yuliana Zamora
This article takes a deep dive into GPUs.
In this series, we cover model deployment: the process of putting models to use. In particular, we’ll see how to package a model inside a web service, allowing other services to use it. We also show how to deploy the web service to a production-ready environment.
