Machine Learning

This book provides a guided tour along the wide range of ML methods that have proven useful in process industry. Step-by-step instructions, supported with real process datasets, show how to develop ML-based solutions for process monitoring, predictive maintenance, fault diagnosis, soft sensing, and process control. Also available at Google Play.

Zefs Guide to Deep Learning is a short guide to the most important concepts in deep learning, the technique at the center of the current artificial intelligence revolution. It will give you a strong understanding of the core ideas and most important methods and applications. All in around only 150 pages!

Selected modern machine learning techniques and the intuition behind them. Methods are supplemented by code snippets with examples in R language. The process is shown through a comic book describing the adventures of two characters, Beta and Bit.  See the flipbook version at https://betaandbit.github.io/RML/

All the statistical foundations you need to understand and use machine learning! It includes R/Pyhton software and Shiny dashboards to illustrate the most important concepts numerically.

Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.

In diesem Primer erklären wir Data Mesh aus der Engineering-Perspektive.

日本語テキストを処理したい全てのプログラマ・エンジニアの方へ。分かち書きなどの基本から、自然言語生成などの最新の話題までをカバー。動かして学べるコードや、参照文献も付いています。言語学や機械学習の知識が無くても問題ありません。

A thorough guide for programmers working with Japanese text, covering fundamental issues like tokenization and recent research topics like generating natural language texts. Working examples are accompanied by extensive reference to allow problem solving even without a background in Japanese or Machine Learning.

Python is a rich and powerful language, but many data scientists merely scratch the surface, and often feel uncertain about what lies beneath.  This book will go deep into the heart of Python, to truly understand its components, and how we can stitch them together to build better scientific workflows and machine learning systems.

Ever wondered what a neural network really is, how it works, or how to implement one? Well, I did, and that is why I tried implementing one. And it was amazing! Now, I just want to help you do it yourself, so that you can take a peek behind the curtains of this world of machine learning, deep learning, and all those buzzwords.

This 15x PDF collection is a compilation of the best cheat sheets created for my free Finxter Email Academy that teaches Python in byte-sized video and cheat sheet lessons.

市面上的深度学习书要么晦涩难懂，要么是平台使用说明书，只讲理论缺少实现或者只有编程缺少原理讲解，都很难让人理解深度学习的基本原理，本书采用理论讲解与代码实现的方式深入浅出地剖析了深度学习的技术原理和实现细节，教会读者如何从零编写一个深度学习库。内容包含了：梯度下降法、回归学习、前馈神经网络、卷积神经网络、循环神经网络、生成网络。

The book explains the core concepts and terminologies of Descriptive Statistics using real-world examples. The book contains 17 practice problems, 2 quizzes, 36 graphical representations and numerous examples to enable effective learning and understanding of the concepts. Please check the bundle options with this book for better deals!

"It is like a voyage of discovery, seeking not for new territory but new knowledge. It should appeal to those with a good sense of adventure," Dr. Frederick Sanger. I hope every reader enjoys this voyage in deep learning and find their adventure.