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 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.
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.
From Automated Machine Learning in Action by Qingquan Song, Haifeng Jin, and Xia Hu
This article covers
• Defining and introducing the fundamental concepts of machine learning
• Describing the motivation for and high-level concepts of automated machine learning
In case you missed it, here is Chris Mattmann and Dr. Scott Penberthy’s live Twitch coding stream recap. For more, check out the book: Machine Learning with TensorFlow, Second Edition. For more live coding streams, subscribe to Manning’s Twitch channel… Continue Reading →
From Machine Learning with TensorFlow, Second Edition by Chris Mattmann This article covers: Building sentiment classifier using logistic regression and with softmax Measuring classification accuracy Computing ROC curve and measure classifier effectiveness Submitting your results to the Kaggle challenge for… Continue Reading →
From Machine Learning with TensorFlow, Second Edition by Chris Mattmann
This article covers using text and word frequency (Bag of Words) to represent sentiment.
The Cool Way to Search Text
By Scott Penbertht and Chris Mattmann
From Machine Learning with TensorFlow, Second Edition by Chris A. Mattmann
