Deep Learning and Practice with MindSpore

This book provides a comprehensive introduction to deep learning theory and its practical applications using the MindSpore AI computing framework. It is divided into 14 chapters and includes examples and links to online resources to help clarify complex topics.

Format: Paperback / softback
Length: 394 pages
Publication date: 19 August 2022
Publisher: Springer Verlag, Singapore

This comprehensive book delves into the realm of deep learning, providing a systematic introduction to its theory and exploring its practical applications within the MindSpore AI computing framework. Spanning 14 chapters, the book comprehensively covers a wide range of topics, including 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 aid in understanding the intricate concepts explored, the book is accompanied by numerous examples and comprehensive links to online resources, making it an invaluable resource for both beginners and experts in the field.

Introduction:
Deep learning has emerged as a powerful tool in the field of artificial intelligence, enabling machines to learn and adapt from vast amounts of data. This book aims to provide a comprehensive guide to the theory and practical applications of deep learning, with a focus on the MindSpore AI computing framework.
Chapter 1: Deep Learning Fundamentals:
In this chapter, we will explore the basic concepts of deep learning, including neural networks, activation functions, and loss functions. We will also discuss the advantages and limitations of deep learning and its applications in various fields.
Chapter 2: Deep Neural Networks (DNNs):
DNNs are the building blocks of deep learning and are used for a wide range of tasks, such as image recognition, speech recognition, and natural language processing. In this chapter, we will delve into the architecture, training, and optimization of DNNs using MindSpore.
Chapter 3: Convolutional Neural Networks (CNNs):
CNNs are particularly effective for image recognition tasks and have revolutionized the field of computer vision. In this chapter, we will explore the architecture, training, and optimization of CNNs using MindSpore.
Chapter 4: Recurrent Neural Networks (RNNs):
RNNs are used for tasks that require sequential data, such as speech recognition and machine translation. In this chapter, we will explore the architecture, training, and optimization of RNNs using MindSpore.
Chapter 5: Unsupervised Learning:
Unsupervised learning is a type of learning where the machine is given unlabeled data and has to find patterns and structures within it. In this chapter, we will explore the principles and applications of unsupervised learning using MindSpore.
Chapter 6: Deep Reinforcement Learning:
Deep reinforcement learning is a type of learning where the machine learns to make decisions by interacting with an environment. In this chapter, we will explore the principles and applications of deep reinforcement learning using MindSpore.
Chapter 7: Automated Machine Learning:
Automated machine learning is a method of building machine learning models without manual intervention. In this chapter, we will explore the principles and applications of automated machine learning using MindSpore.
Chapter 8: Device-Cloud Collaboration:
In today's interconnected world, data is often stored and processed on different devices and in the cloud. In this chapter, we will explore the principles and applications of device-cloud collaboration using MindSpore.
Chapter 9: Deep Learning Visualization:
Visualization is an important aspect of understanding and interpreting deep learning models. In this chapter, we will explore the principles and applications of deep learning visualization using MindSpore.
Chapter 10: Data Preparation for Deep Learning:
Data preparation is a critical step in the process of building and training deep learning models. In this chapter, we will explore the principles and applications of data preparation for deep learning using MindSpore.
Conclusion:
Deep learning has the potential to transform a wide range of industries and applications, and this book provides a solid foundation for anyone interested in learning and applying this powerful technology. By leveraging the MindSpore AI computing framework, readers will gain the skills and knowledge needed to build and deploy deep learning models in real-world scenarios.

Weight: 629g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811622359
Edition number: 1st ed. 2021