Books

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.

How to transform machine learning research into successful production systems? The answers are in this book! Learn real stories, architectural concepts, tools and best practices that catalyze individual and organizational success in ML.

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.

A beginner-friendly introduction to machine learning with Python, that is based on the PyCaret and Streamlit libraries. Readers will delve into the fascinating world of artificial intelligence, by easily training and deploying their ML models!

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.

I write this book for all the people who believe that link between technology and humans can create a better world. I believe that fast advancement of the technology will help our planet to solve many problems in a more fast smart way that what human brain is capable to do.All this because of fast development of microprocessor architecture.

Learn how to implement various feature selection methods in a few lines of code and train faster, simpler, and more reliable machine learning models. Using Python open-source libraries, you will learn how to find the most predictive features from your data through filter, wrapper, embedded, and additional feature selection methods.