Machine Learning

Learn how to build your own AI application step-by-step. A hands-on guide to AI development with local LLM inference

This is part 2 of my 3-part training guide on how to build self-driving cars from scratch. This guide is bundled with a repository containing simulations, python scripts, graphics, and other useful assets.

Discover the magic of AI with ease! "ChatGPT for the Layman: Practical Applications and Exercises for Everyday Use" demystifies AI with simple explanations, real-life uses, and hands-on exercises. Learn to draft emails, brainstorm ideas, and boost productivity effortlessly. Join the AI revolution and transform your daily life with ChatGPT today!

Unlock the power of linear algebra in your data science projects! "Linear Algebra Essentials for Data Scientists" dives into the key concepts driving data analysis and machine learning. With clear explanations, practical Python examples, and hands-on exercises, this book is your gateway to mastering the mathematical foundations of data science.

JupyterLab, the latest iteration of the Jupyter Notebook, is a versatile tool for sharing code in an easily understandable format. Jupyter Notebooks can be emailed, put on GitHub, or run online. You may also add HTML, images, Markdown, videos, LaTeX, and custom MIME types to your Notebooks. Finally, Jupyter Notebooks support big data integration. JupyterLab 101 will get you up to speed on the newest user interface for Jupyter Notebooks and the other tools that JupyterLab supports. You now have a tabbed interface that you can use to edit multiple Notebooks at once.

This is part 1 of my 3-part training guide on how to build self-driving cars from scratch. This guide is bundled with a repository containing simulations, python scripts, graphics, and other useful assets . In this step-by-step guide, I’ll teach you how to make an app that you can use to create a simulation where cars learn how to drive autonom...

This book is meant to discover how the language Rust, which is known for being fast and safe, can work with the constantly changing field of machine learning, neural networks and deep learning. The datasets of COVID, CIFAR-10 and practical examples are meant to show how Rust's features can be used to solve problems that come up in machine learning.

This book provides a guided tour of ML techniques utilized in process industry for plant health management. Step-by-step instructions, supported with industrial-scale process datasets, show how to develop ML-based solutions for equipment condition monitoring, plantwide monitoring, and predictive maintenance solutions. Also available at Google Play 

Explore Python basics, AI integration for real-world application and the power of Python in AI with practical machine learning and deep learning techniques.

This book is a journey that takes you from the foundational basics to the advanced techniques, with real-world examples and datasets at every turn. What sets this book apart is its focus on the challenges and intricacies of building and fine-tuning neural networks. You'll learn to build these networks and also to make them efficient and effective.

Put machine learning and neural networks into practice using production oriented, distributed trained, and rich robust ecosystem of python tools

Embark on a captivating six-decade odyssey through the ever-evolving universe of patterns. From the birth of design patterns in the early days of software development to the cutting-edge patterns shaping our future, this book is your ultimate guide to the silent architects behind the IT world's most successful innovations.

This book was designed with the novice Python programmer and/or information technology professional who doesn't have programming experience, but wants or needs to learn Python to do jobs or tasks related to the programming language. If you need to learn Python for tasks such as data scrubbing, network automation, web development, etc.

This book bridges theory and practice in PD rating modeling, offering practical steps, real-world examples, and a focus on design. It enables readers to shape customized solutions for diverse institutions, transforming the landscape of credit risk modeling.

The book navigates through the complexities of deploying TensorFlow models into production including sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness.