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.

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.

Welcome to one of the On Weekend Series, this series helped me a lot before, and I hope it helps you now. And this book is about the techniques that you should learn to learn, understand and bulid a convolution neural network. I hope you find what you need in this book.

Learn to master the Google Cloud platform (GCP).

"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.

Python has grown in recent years, due to trends of data science and ML, and I think the main reason is because it’s essentially for a person to keep track of everything that is going on. And it makes “introducing” people to Python an interesting. Nowadays, audiences need to learn python in fast way for data science and that what we will give them.

tldr: Don't read this if all you want is from tensroflow import keras

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.

Learn the computer science foundations you need to jump start your career. New second edition with expanded content and new chapters on machine learning, deep learning and AI & LLMs. Thirteen chapters covering all the career-essential topics. Perfect for self-taught developers or anyone who wants to really understand how computers work.

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.