Neural Networks and Deep Learning by Michael Nielsen
Neural Networks and Deep Learning - Table of Contents
1. Using Neural Nets to Recognize Handwritten Digits
2. How the Backpropagation Algorithm Works
3. Improving the Way Neural Networks Learn
4. A Visual Proof that Neural Nets Can Compute Any Function
5. Why Are Deep Neural Networks Hard to Train?
6. Deep Learning
A. Is There a Simple Algorithm for Intelligence?
What You Will Learn in Neural Networks and Deep Learning
"Neural Networks and Deep Learning" by Michael A. Nielsen is a clear and practical guide to understanding "neural networks", "deep learning", and modern "machine learning". The book introduces the key principles behind neural networks, showing how they learn patterns from data and why they are effective in tasks like image recognition, speech processing, and natural language understanding. Written in an accessible style, it makes complex concepts understandable even for beginners, without requiring an advanced math background.
The book covers essential topics such as perceptrons, activation functions, backpropagation, optimization techniques, and overfitting. Nielsen uses intuitive explanations, visual examples, and practical code to demonstrate how deep learning models are trained and applied. Readers learn how to design neural network architectures, adjust parameters, and evaluate model performance, bridging the gap between theory and practical implementation. Emphasis is placed on developing both conceptual understanding and hands-on skills for solving real-world problems using deep learning.
Overall, this book is ideal for students, programmers, and anyone interested in artificial intelligence. It equips readers with the knowledge to implement "training algorithms", understand "network architecture", and apply "pattern recognition", "predictive modeling", and deep learning techniques in practice. By combining theory, examples, and exercises, it builds a strong foundation in "artificial intelligence" and prepares learners to explore advanced topics in AI and neural networks.
Book Details & Specifications
Title:
Neural Networks and Deep Learning by Michael Nielsen
Publisher:
Determination Press
Year:
2016
Pages:
224
Type:
PDF
Language:
English
ISBN-10 #:
3031296419
ISBN-13 #:
978-3031296413
License:
External Educational Resource
Amazon:
Amazon
About the Author: Michael Nielsen
The author
Michael Nielsen
is an Australian physicist and computer scientist born in 1974. He studied "physics" at the University of Queensland and earned his "PhD in Quantum Information Theory" from the University of New Mexico. Nielsen is known for his work in "quantum computation" and co-authoring the seminal textbook "Quantum Computation and Quantum Information". His expertise includes "neural networks, deep learning, machine learning, scientific communication, and open science advocacy". He authored "Neural Networks and Deep Learning" to provide a practical, intuitive guide to AI concepts. Nielsen’s work bridges theory and application, helping students and researchers understand and implement neural network models effectively.
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