The Best Neural Networks Books 2024

1. Pre-Owned, Guide to Networking Essentials, (Paperback)

Pre-Owned - GUIDE TO NETWORKING ESSENTIALS provides students with both the knowledge and hands-on skills necessary to work with network operating systems in a network administration environment. By focusing on troubleshooting and computer networking technologies, this book offers a comprehensive introduction to networking and to advances in software, wireless and network security. Challenge Labs and Hands-On Projects are directly integrated in each chapter to allow for a hands-on experience in the classroom. Updated content reflects the latest networking technologies and operating systems including new Ethernet standards, cloud computing, Windows 10, Windows Server 2016, and recent Linux distributions.

Pre-Owned, Guide to Networking Essentials, (Paperback)

2. Pre-Owned Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and (Paperback 9781491962299) by Aurélien Géron

9781491962299. Pre-Owned: Good condition. Trade paperback. Language: English. Pages: 572. Trade paperback (US). Glued binding. 572 p. Through a series of recent 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 practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks-scikit-learn and TensorFlow-author Aurelien Geron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details

Pre-Owned Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and (Paperback 9781491962299) by Aurélien Géron