Books

هل تريد تعلم خوارزميات تعلم الالة بشكل عملي واضح وتطبيقي. في هذا الكتاب ستجد مرجع لخوارزميات تعلم الآلة مع أمثلة تطبيقية بلغة بايثون بلغة واضحة وسهلة الفهم

Welcome to the first book in the Week End 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 deal with data, any data scientist, and not only data scientists, but every one that faces data problems

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).

Dive into NLP, deep learning, knowledge representation, and semantic web technologies. All of my Leanpub books, including this book, can be read for FREE on my web site: https://markwatson.com/

"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.

All examples in Hy. The Hy language (Lisp that compiles to Python) allows Lisp programmers access to the rich Python ecosystem for Large Language Models, deep learning, artificial intelligence, and general data wrangling. Applications: LangChain, Knowledge Graphs, NLP, Deep Learning.

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!