Fundamentals of Deep Learning 2017 英文高清.pdf版

Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms by Nikhil Buduma and Nicholas Locascio English | 2017 | ISBN: 1491925612 | 304 pages | PDF | 15,2 MB With the reinvigoration of neural networks in the 2000s, deep learning has become an extremely active area of research that is paving the way for modern machine learning. This book uses exposition and examples to help you understand major concepts in this complicated field. Large companies such as Google, Microsoft, and Facebook have taken notice, and are actively growing in-house deep learning teams. For the rest of us however, deep learning is still a pretty complex and difficult subject to grasp. If you have a basic understanding of what machine learning is, have familiarity with the Python programming language, and have some mathematical background with calculus, this book will help you get started. Table of Contents Chapter 1. The Neural Network Chapter 2. Training Feed-Forward Neural Networks Chapter 3. Implementing Neural Networks in TensorFlow Chapter 4. Beyond Gradient Descent Chapter 5. Convolutional Neural Networks Chapter 6. Embedding and Representation Learning Chapter 7. Models for Sequence Analysis Chapter 8. Memory Augmented Neural Networks Chapter 9. Deep Reinforcement Learning