Deep Learning is a virtual extension of artificial intelligence. It has quite a high application in human-machine communication.
Deep Learning is also a rather complex field. If you want to develop a career in this field, you must build a solid foundation and keep updating your knowledge.
The ten books in the article are valuable resources you can refer to understand better the field you are pursuing.
Each book on the list deals with subtopics in Deep Learning. You’ll need to understand programming, statistics, and math to get an edge when working in deep learning.
The following books will cover all of the above knowledge in the most understandable and applicable way.
1. Deep Learning
Specification:
- Language: English.
- Paperback: 800 pages.
- Item Weight: 2.54 pounds.
- Dimensions: 9.1 x 7.2 x 1.1 inches.
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Reason To Buy
- Mention and fully explain related concepts.
- An introduction to topics related to deep learning.
- Describe many deep learning techniques.
- Suitable for both beginners and industry experts.
- Cover many advanced study topics
Review
I got to know Deep Learning through presentations by Elon Musk, co-founder and CEO of Tesla and SpaceX. According to this billionaire, it is the complete book on Deep Learning.
This one covers and explains all the concepts related to a severe learning problem. It also provides a detailed introduction to the prominent topics in deep learning: mathematical foundations, machine learning, and others.
This book also describes several deep-learning techniques used by industry experts. It is my favorite part of the book due to its helpful information about optimization algorithms, deep-forward networks, and convolutional networks.
With vast knowledge, Deep Learning with Adaptive Computation is helpful to both beginners and senior software engineers who want to apply this technology to their products and platforms.
2. Deep Learning for Natural Language Processing
Specification:
- Language: English.
- Paperback: 296 pages.
- Item Weight: 1.23 pounds.
- Dimensions: 7.38 x 0.7 x 9.25 inches.
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Reason To Buy
- No deep learning experience is required.
- Explicit mention of NLP algorithms.
- Tutorial on how to create real-world NLP applications.
- Instructions for using many advanced equipment tools.
- Answer all your questions about NLP.
Review
Deep Learning enables computer systems to complete complex tasks that require understanding and context. In particular, deep learning can innovate summarization and connection creation by natural language processing.
And Deep Learning for Natural Language Processing is one of the best books dealing with NPL algorithms.
This book focuses on creating advanced NLP applications using tools and devices such as Python, the Keras deep learning library, BERT, and XLNET.
In addition to providing detailed step-by-step instructions, the book also includes many compelling examples to help readers quickly grasp information about NLP applications in the real world.
This book answers all of your NLP questions. It will also aid in improving performance by guiding readers to learn language multitasking.
3. Deep Learning with Python
Specification:
- Language: English.
- Paperback: 384 pages.
- Item Weight: 3.53 ounces.
- Dimensions: 7.38 x 0.8 x 9.25 inches.
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Reason To Buy
- Intuitive explanation.
- Mighty Keras library manual.
- Provide in-depth knowledge of the Python language
- The reader will be able to create applications in computer vision.
- Assist readers in setting up a deep learning environment.
Review
Deep Learning with Python is a book that requires no prior experience with Keras and TensorFlow. Yet I recommend that you have intermediate Python skills to read and understand the content in this book.
If you have a previous Python background, you will learn a lot from this book.
One of the things I appreciate most about this book is that it provides in-depth knowledge of the Python language and the powerful Keras library.
The author also remembers to add visual explanations and practical examples to create applications in computer vision and general models.
This book also assists readers in setting up their deep learning environments. In the book’s chapters, I also learned a lot about deep learning for text and sequence and image classification models.
4. Advanced Deep Learning with Keras
Specification:
- Language: English.
- Paperback: 368 pages.
- Item Weight: 1.39 pounds.
- Dimensions: 7.5 x 0.83 x 9.25 inches.
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Reason To Buy
- A guide to advanced deep learning techniques.
- Overview of MLP, CNN, and RNN.
- Instructions on how to implement DRL.
- Refer to advanced courses in human-like AI performance
- Help readers apply their Keras for TensorFlow 1.x l knowledge.
Review
As the name suggests, this is a comprehensive book on advanced deep-learning techniques. In the first pages, the book mentioned quite complex knowledge about autoencoders, deep reinforcement learning, etc.
If you have never studied deep understanding before, you may encounter some difficulty absorbing the knowledge shared by the author.
However, if you have ever studied deep learning, you will surely get the most impressive AI results after applying the knowledge in the book.
In addition to the advanced knowledge, this book engages me with research on GANs, deep learning techniques, and deep neural network implementations.
5. Hands-On Deep Learning Algorithms with Python
Specification:
- Language: English.
- Paperback: 512 pages.
- Item Weight: 1.92 pounds.
- Dimensions: 7.5 x 1.16 x 9.25 inches.
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Reason To Buy
- Provide knowledge of various autoencoders
- Explain the algorithm in an easy-to-understand way.
- Dive into the application of mathematics in neural networks.
- Provide skills for readers to implement deep learning in projects.
- A guide to implementing the most popular deep learning algorithms.
Review
Learning linear algebra and probability will help you approach deep learning more efficiently. And Hands-On Deep Learning is one of the few books that delve deeply into math and its application in building neural networks.
I was incredibly impressed with the way the author shared about mathematical principles. Deep learning algorithms are complex, but the author has simplified everything so non-experts can grasp the basic principles.
This book explains the principle and includes instructions for implementing the most popular deep learning algorithms.
Through the TensorFlow tutorials, the book showed me that implementing CNNs and RNNs is more straightforward than I thought it would be.
6. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
Specification:
- Language: English.
- Paperback: 850 pages.
- Item Weight: 2.97 pounds.
- Dimensions: 7.25 x 2 x 9.5 inches.
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Reason To Buy
- Give many examples.
- TensorFlow Library User’s Guide.
- A detailed guide to neural network architecture.
- Provides two production-ready Python frameworks
- The system of exercises is rich and highly applicable.
Review
Are you a programmer who has yet to learn too much about deep learning? Hands-On Machine Learning may be a helpful book you need.
Unlike many books I’ve recommended, Hands-On Machine Learning doesn’t delve into the theoretical presentation. It focuses on giving examples and provides two production-ready Python frameworks.
With the tools the book provides, I have learned how to build an intelligent system using the most straightforward tools.
Another advantage I appreciate in this book is that the system of exercises at the end of each chapter is quite diverse. You should not miss any practices because they are an opportunity to help you apply what you have learned to complete your project.
7. Deep Learning for Coders
Specification:
- Language: English.
- Paperback: 624 pages.
- Item Weight: 3.53 ounces.
- Dimensions: 6.9 x 1.1 x 9.1 inches.
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Reason To Buy
- Knowledge is highly applicable.
- Provide a complete theoretical basis.
- The guides are engaging and interesting.
- Guide readers to create high-performance models.
- Suitable for people with basic knowledge of coding.
Review
Deep learning was a complex field in the past, only suitable for people who have at least worked in the technology field or have a Ph.D. However, Deep Learning for Coders changed my mind.
The book goes from deep learning theory to algorithm analysis, natural language processing tutorials, and tabular data. The knowledge it provides helps me improve the accuracy and speed of my work with deep learning models.
To read this book, you need at least some programming knowledge. For this reason, I encourage programmers familiar with Python to read this helpful book.
8. Machine Intelligence
Specification:
- Language: English.
- Paperback: 234 pages.
- Item Weight: 12.3 ounces.
- Dimensions: 6 x 0.53 x 9 inches.
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Reason To Buy
- Simple approach.
- Provide an AI overview
- Suitable for many objects.
- The presentation is easy to understand and attractive.
- Includes both general and specialized principles
Review
Artificial intelligence is one of the most promising fields today. It is also a rather complex field that is quite difficult to master.
And if you want to learn and have the best overview of this vast field, Machine Intelligence is the book you should not miss.
Whether you are an expert, a student of information technology, or someone looking to learn about machine technology, you will probably love this book.
The author mentions the most basic knowledge about AI and ML in a reasonably scientific and straightforward way. While reading this book, you won’t be fooled by complicated math equations or confusing algorithmic concepts.
9. Neural Networks for Pattern Recognition
Specification:
- Language: English.
- Paperback: 502 pages.
- Item Weight: 1.66 pounds.
- Dimensions: 1.12 x 9.19 x 6.19 inches.
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Reason To Buy
- More than 100 exercises.
- Only the basic concepts are covered.
- Detailed step-by-step instructions.
- Explain the mathematical bases clearly.
- Approaching neural networks from a pattern recognition perspective.
Review
The author of Neural Networks – Pattern Recognition only spends a few pages covering the basics. After completing the introduction, the book has gone into depth, teaching and practicing techniques for modeling probability density functions and error function minimization steps.
The book also includes more than 100 exercises. It is its most significant advantage, requiring readers to practice regularly applying the book’s knowledge to real projects.
10. Neural Smithing
Specification:
- Language: English.
- Paperback: 352 pages.
- Item Weight: 1.85 pounds.
- Dimensions: 9.36 x 7.25 x 1.03 inches.
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Reason To Buy
- Fascinating opening.
- Focus on MLP multilayer perceptron.
- Provide an overview of MLP
- The knowledge in the book is highly applicable.
- It does not require the reader to understand neural computing.
Review
Neural Smithing does not include all knowledge related to feedforward artificial neural networks.
It focuses on a small aspect of artificial neural networks, the MLP multilayer perceptron. The book begins with inspirational questions about the nature of MLP. The author then gave me all aspects of the MLP methodology.
I use this book as a reference and tool to deal with specific problems in my MLP application.
Buying Guide
Content
I always appreciate books that combine theory and practice. Because deep learning is a particular field, it requires learners to practice regularly to understand essential ideas.
User Reviews
You can refer to the reviews from readers who have read the book before to understand the book content in depth.
In particular, pay attention to the evaluations of people with the same level and learning goals as you.
Publication date
Deep Learning is a rapidly changing field. Therefore, it is better to look to highly up-to-date books to notice the current artificial intelligence trend.
FAQs
Which is best for deep learning?
You have quite a few methods to learn about deep learning. In addition to books, you can find courses, video materials, and notes on real projects.
Is Deep Learning with Python a good book?
It is an in-depth presentation of Python in deep learning. This book is especially relevant for programmers or anyone with experience working with Python.
Who is the father of deep learning?
Frank Rosenblatt is the father of deep learning systems. He is the one who brings excellent contributions to the development and construction of the essential components of deep learning methods.
Should I learn deep or AI first?
You should learn about AI first if you want to learn deeply about natural language processing and computer vision.
Why is deep learning popular?
Deep Learning became popular because it minimizes the need for human action, making the process faster and minimizing risk.
Do I need to know Python before going into Deep learning?
It would help if you learned about Python before machine learning because your Python knowledge will help you handle data types and models better.
Does Deep Learning involve mathematics?
Deep Learning, like any other machine learning, uses complex algorithms. As a result, deep learning researchers deeply understand mathematics, incredibly linear algebra, and calculus.
Are books an excellent source to get started with deep learning?
The book is an ideal resource when learning about deep learning. You can also find some other resources like courses and lectures.
Conclusion
If you are interested in deep learning math, Hands-On Deep Learning Algorithms with Python is a fascinating book. Meanwhile, Deep Learning with Python is an attractive book for those with programming skills.
Whatever your interest, I hope you find the ideal book after consulting the list above. Thank you for reading!