Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Thru a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now,even programmers who know close to nothing about this technology can use simple,efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples,minimal theory,and production-ready Python frameworks (scikit-Learn,Keras,and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. With this updated third edition, author Aurélien Géron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. The book focuses on:
- Using Scikit-learn to track an example ML project end-to-end
- Exploring models like support vector machines, decision trees, random forests, and ensemble methods
- Exploiting unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Diving into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and more
- Building and training neural nets for various applications using TensorFlow and Keras
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The Hundred-Page Machine Learning Book (The Hundred-Page Books)
The Hundred-Page Machine Learning Book
Master machine learning through clarity, not complexity in a book engineered for exceptional conciseness. This essential book delivers a complete education in modern machine learning, focusing on practical applications and basic algorithms, including deep learning and neural networks. It features a careful progression through key concepts, starting from essential mathematics and advancing to critical machine learning algorithms. Readers will grasp practical skills like feature engineering, handling imbalanced datasets, and model evaluation, overcoming the overwhelming complexity often found in technical literature. This isn’t just another theoretical textbook; every chapter reflects the author’s real-world experience,making it valuable for both newcomers and seasoned practitioners. The book assumes a basic mathematical foundation, yet introduces all necessary concepts in an intuitive manner, ensuring accessibility. Endorsed by leaders in the field, it provides an engaging roadmap for mastering machine learning.
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Why Machines Learn: The Elegant Math Behind Modern AI
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Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications
Machine learning systems are both complex and unique. Complex because they consist of many different components and involve many different stakeholders. Unique because they’re data-dependent, with data varying wildly from one use case to the next. In this book,you’ll learn a holistic approach to designing ML systems that are reliable,scalable,maintainable,and adaptive to changing environments and business requirements. Author Chip Huyen, co-founder of Claypot AI, considers each design decision-such as how to process and create training data, which features to use, how often to retrain models, and what to monitor-in the context of how it can help your system as a whole achieve its objectives. The iterative framework in this book uses actual case studies backed by ample references. This book will help you tackle scenarios such as:
- Engineering data and choosing the right metrics to solve a business problem
- Automating the process for continually developing, evaluating, deploying, and updating models
- Developing a monitoring system to quickly detect and address issues your models might encounter in production
- Architecting an ML platform that serves across use cases
- Developing responsible ML systems
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