Machine Learning

Programming of distributed and Web crowdsourcing applications using mobile agents and the JavaScript Agent Machine can be so easy! Less than 100 lines code are required to create a multi-agent system. Only basic JavaScript knowledge is required.

¿Estás buscando un libro con el que puedas aprender sobre aprendizaje profundo y PyTorch sin tener que pasar horas descifrando texto y código críptico? ¿Un libro técnico que sea también legible y entretenido? ¡Aquí lo tienes!

Get to know how to deploy small applications and machine learning solutions using the serverless.com framework.

Zefs Guide to Deep Learning Flashcards is a set of digital flashcards that accompany the book Zefs Guide to Deep Learning. Anyone wanting to improve their knowledge of the key concepts in machine learning and deep learning will benefit from studying with these flashcards, whether to land that dream AI job or ace their machine learning exams.

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.

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

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.