As we traverse our digital landscape, we leave trails in these tables that, if deciphered correctly, can unveil countless insights. Welcome to our book, Machine Learning on Tabular Data, a key guide to unlocking these mysteries. Dive in and navigate the ever-evolving dynamics of machine learning and deep learning, exclusively focused on this crucial data form. By the time you turn the last page, you’ll be prepared to transform columns and rows into actionable strategies and insights.
From Fight Fraud with Machine Learning by Ashish Ranjan Jha Step into the age of AI-powered fraud detection with Fight Fraud with Machine Learning, where every challenge is an opportunity to innovate. This comprehensive guide seamlessly blends theory with hands-on… Continue Reading →
With the Statistics Playbook by Gary Sutton
This book will teach you how to use R in a different way than any other book out there. You will approach R concepts using publicly-available NBA statistical data rather than prepared datasets, and learn how to combine various methods and techniques.
Read on to learn more.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
· Storing values on keys in dictionaries.
· Working with pair objects.
· Using tuples to create dictionaries.
· Comparing dictionaries and arrays.
Read it if you’re interested in the Julia language or in how it handles dictionaries.
An excerpt from Managing Machine Learning Projects by Simon Thompson
Managing Machine Learning Projects will teach you to guide machine learning projects from design to production—no machine learning experience required!
Read this article if you’re a project manager who works with machine learning applications.
An excerpt from Julia as a Second Language by Erik Engheim
This article covers:
What type of problems Julia solves.
The limits of statically-typed languages.
Why the world needs a fast dynamically-typed language.
How Julia increases programmer productivity.
Read it if you’re interested in the Julia language and its strengths and weaknesses.
From Distributed Machine Learning Patterns by Yuan Tang
In this article, we introduce the collective communication pattern, which is a great alternative to parameter servers when the machine learning model we are building is not too large without having to tune the ratio between the number of workers and parameter servers.
From Distributed Machine Learning Patterns by Yuan Tang
In this article, we introduce the parameter server pattern which comes handy for situations where the model is too large to fit in a single machine such as one we would have to build for tagging entities in the 8 millions of YouTube videos.
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.
From Machine Learning Bookcamp by Alexey Grigorev
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.