Books

This machine learning book series aims at providing real hands-on training from general concepts and architecture to low-level details and mathematics. The first epoch covers the simplest linear associative network, proposes a brick notation for algebraic expressions, shows required calculus derivations, and illustrates gradient descent.

Google engineers are regular expression masters. Do you want to become one, too? The Smartest Way to Learn Python Regex transforms you into a regular expression master. The book leverages an innovative learning approach: (1) read a chapter, (2) watch a course video, and (3) solve a code puzzle. It's fun!

In Errors of Regression Models you’ll learn how to choose the most appropriate statistics to measure the accuracy of your regression-based prediction model.Written in plain English with no technical jargon, Errors of Regression Models is perfect for beginners!Discover how to measure the accuracy of your regression models quickly and effectively.

This book contains detailed stepwise solutions to Regression problems for beginners. It is a collection of solutions in Python and R programming language.

Need to collect terabytes of data across all aspects of your operations? Need to transform complex scientific datasets into innovative software that is deployed across the pipeline, accelerating the pace and quality of all business decisions to unbelievable levels? Having problems with performance or maintenance? If so, this book is for you!

"Success in creating AI would be the biggest event in human history. Unfortunately, it might also be the last, unless we learn how to avoid the risks." - Stephen Hawking. Learn how to solve real-world problems with Deep Learning models (NLP, Computer Vision, and Time Series). Go from prototyping to deployment with PyTorch and Python!

These three-volume book series cover a wide variety of topics in machine learning focusing on supervised and unsupervised learning, intended for data scientist and machine learning experts providing a very concise description of the scikit-learn library. The first volume covers the generalized linear models (linear & logistic regression).

This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. Many resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. Based on material taught at leading universities.

Whether you want to learn Machine Learning for your work or research or you want to become a master, so the others pay you to do it, you need to know how it works. For knowing how it works, you NEED TO KNOW Linear Algebra, which is the foundation of Machine Learning. BUT Linear Algebra is too boundless! In this book, you will get what is NECESSARY.

Delve into the great ecosystem of CUDA frameworks and libraries through independent projects.Solve modern, real-world technical applications using CUDA.Interesting projects that will help you build High-Performance applications with CUDA.

Do Machine Learning Yourself is a collection of do it yourself (DIY) projects about machine learning, mainly about computer vision, for beginner and intermediate levels. Through a detailed guidance per each project, everything required to do that project yourself will be clear. A focus is to make the projects run in mobile devices.

This book brings the fundamentals of Machine Learning to you, using tools and techniques used to solve real-world problems in Computer Vision, Natural Language Processing, and Time Series analysis. The skills taught in this book will lay the foundation for you to advance your journey to Machine Learning Mastery!

Intelligence is problem solving. Is that all which can be said about it? How do natural and artificial intelligence differ? And what has consciousness or even life to do with it? How does AI basically work? What is its benefit and disadvantage? How much do we need AI? These questions and more are discussed and to most of them an answer is given.

"If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book." —Cassie Kozyrkov, Chief Decision Scientist at Google "Foundational work about the reality of building machine learning models in production." —Karolis Urbonas, Head of Machine Learning and Science at Amazon

This book teaches the different types of convolution operators to design Deep Neural Networks with a variety of illustrative figures and examples. The basic concepts of the convolution operator to its advanced types are described with full details in this book. Reading this book is recommended to all researchers, and engineers in this field.