What books are suitable for beginners of deep learning?

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What books are suitable for beginners of deep learning? [Copy link]

 

What books are suitable for beginners of deep learning?

caiyao

For beginners, the following books are suitable for introductory deep learning:

1. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville: This book is considered one of the bibles in the field of deep learning. It is very detailed and covers the basic principles and algorithms of deep learning.

2. Neural Networks and Deep Learning by Michael Nielsen: This book is free to read online and is suitable for beginners. The content is easy to understand and covers the basic concepts and practical techniques of neural networks and deep learning.

3. Deep Learning for Beginners: Practical Guide with Python by George Lungu: This book introduces deep learning from both theoretical and practical aspects, combined with Python code examples, which is suitable for beginners to quickly get started.

4. Python Deep Learning by Ivan Vasilev, Daniel Slater, Gianmario Spacagna, and Peter Roelants: This book introduces the basics and techniques of deep learning using Python, including practical applications of popular frameworks such as TensorFlow and Keras.

5. "Beauty of Deep Learning" by Zheng Zeyu: This book combines theory and practice, and helps readers deeply understand the principles and applications of deep learning through a large number of cases and experiments.

These books can be used as reference materials for beginners to get started with deep learning. You can choose books that suit you to read and study according to your interests and learning methods.

路过打酱油

The following books are recommended for beginners to get started with deep learning. These books cover everything from basic concepts to practical applications, helping beginners gradually master the core knowledge and skills of deep learning.

1. Deep Learning

• Author : Ian Goodfellow, Yoshua Bengio, Aaron Courville
• Introduction : This book is known as the "Bible" in the field of deep learning. It systematically introduces the basic theories and key technologies of deep learning, covering the basic concepts of neural networks, optimization methods of deep learning, and various deep learning models. It is suitable for readers with a certain foundation in mathematics and machine learning.
• Recommended reason : It is highly authoritative, comprehensive in content, and suitable as a systematic learning material for deep learning.

2. Neural Networks and Deep Learning

• By Michael Nielsen
• Introduction : This book introduces the basics of neural networks and deep learning in an easy-to-understand way. It guides readers step by step to build and understand neural network models, and explains the mathematical principles behind them in an easy-to-understand way.
• Recommended reason : Suitable for beginners to read, it can help readers quickly understand the core concepts and practical skills of deep learning.

3. Dive into Deep Learning

• Author : Li Mu, Aston Zhang, Zachary C. Lipton, Alexander J. Smola
• Introduction : This is a very practical book that uses Jupyter Notebook as the programming environment and demonstrates the basic principles and applications of deep learning through actual code. The book covers a large number of exercises and practical cases to help readers master deep learning technology through hands-on practice.
• Recommended reason : It is practice-oriented and rich in code examples, making it very suitable for readers who want to learn deep learning through hands-on operations.

4. Deep Learning with Python

• By Franois Chollet
• Introduction : Written by the author of the Keras framework, this book introduces the basics and practical techniques of deep learning using Python. The book includes many real-world cases and code examples that show how to build and train deep learning models.
• Recommended reason : Keras is a concise and powerful deep learning framework. This book is suitable for both beginners and readers with a certain foundation, especially for readers who like to learn through practice.

5. Introduction to Deep Learning: Theory and Implementation Based on Python

• Author : Yasuki Saito
• Introduction : This book starts with basic mathematical knowledge and combines Python programming language to gradually explain the basic knowledge and implementation methods of deep learning. The examples and codes in the book are very easy to understand and use.
• Recommended reason : Suitable for beginners, especially those who do not have a solid foundation in mathematics and programming. Through this book, you can gradually build a comprehensive understanding of deep learning.

6. Machine Learning Yearning

• By Andrew Ng
• Introduction : This book is not a textbook, but a practical guide on how to apply machine learning and deep learning to practical problems. The book shares many experiences and techniques on model selection, parameter adjustment, data processing, etc.
• Recommended reason : Written by Andrew Ng, an authority in the field of deep learning, this book is suitable for readers who want to apply deep learning to practical projects.

Summarize

The above recommended books cover all aspects from basic theory to practical application, and are suitable for beginners with different backgrounds and needs. Through systematic learning and practical operation, readers can quickly master the core knowledge of deep learning and start their exploration journey in this field.

鹅鹅鹅鹅

For beginners, some books that are clear and easy to understand and cover the basics may be the most suitable. Here are a few books suitable for beginners of deep learning:

1. Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

   • This book is considered one of the bibles in the field of deep learning. It covers the basic principles of deep learning, common models (such as neural networks, CNN, RNN, etc.), optimization algorithms, etc., and provides rich examples and practical guidance.

2. Deep Learning with Python by Franois Chollet

   • Written by one of the founders of Keras, this book is based on Python and introduces how to use Keras to build and train deep learning models. The book contains a wealth of code examples and practical projects, making it suitable for beginners to get started quickly.

3. Neural Networks and Deep Learning: A Textbook by Charu C. Aggarwal

   • This book focuses on the basic concepts, mathematical principles, and practical applications of neural networks and deep learning. It provides in-depth theoretical explanations and rich examples, suitable for readers who want to have a deeper understanding of deep learning.

4. Deep Learning from Scratch: Building with Python from First Principles by Seth Weidman

   • This book introduces the basic concepts and implementation principles of deep learning from scratch, as well as how to use Python and NumPy to build deep learning models. It is suitable for beginners to understand the basic principles and implementation methods of deep learning.

5. "Introduction to Deep Learning: PyTorch Practice" by: Li Mu, Aston Zhang, Zack C. Lipton, Mu Li

   • This book is based on PyTorch and introduces the basic principles and practical methods of deep learning. The book contains rich code examples and practical projects, suitable for readers who want to use PyTorch for deep learning.

The above books are classics in the field of deep learning. They cover the basic knowledge and practical methods of deep learning and are suitable for beginners. You can choose one or more of them to study according to your interests and needs.

fjdeepblue

Very good electronic information, the summary is very detailed and has reference value. Thank you for sharing