Description
This book systematically introduces readers to the theory of deep learning and explores its practical applications based on the MindSpore AI computing framework. Divided into 14 chapters, the book covers deep learning, deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), unsupervised learning, deep reinforcement learning, automated machine learning, device-cloud collaboration, deep learning visualization, and data preparation for deep learning. To help clarify the complex topics discussed, this book includes numerous examples and links to online resources.
Table of Contents
Chapter 1. Introduction.- Chapter 2. Deep Learning Basics.- Chapter 3. DNN.- Chapter 4. Training of DNNs.- Chapter 5. Convolutional Neural Network.- Chapter 6. RNN.- Chapter 7. Unsupervised Learning: Word Vector.- Chapter 8. Unsupervised Learning: Graph Vector.- Chapter 9. Unsupervised Learning: Deep Generative Model.- Chapter 10. Deep Reinforcement Learning.- Chapter 11. Automated Machine Learning.- Chapter 12. Device-Cloud Collaboration.- Chapter 13. Deep Learning Visualization.- Chapter 14. Data Preparation for Deep Learning.
