Are you looking for the **Best Books on Neural Networks and Deep Learning**?. If yes, then read this article. In this article, I have listed the **10 Best Books on Neural Networks and Deep Learning**. And I will also guide you to choose the best book for you.

Now without wasting your time, let’s get started-

- 1. Deep Learning (Adaptive Computation and Machine Learning series
- 2. Deep Learning with Python
- 3. Neural Networks and Deep Learning
- 4. Hands-On Deep Learning Algorithms with Python
- 5. Deep Learning: A Practitioner's Approach
- 6. Hands-On Machine Learning with Scikit–Learn and TensorFlow
- 7. TensorFlow 1.x Deep Learning Cookbook
- 8. Neural Networks for Pattern Recognition
- 9. The Hundred-Page Machine Learning Book
- 10. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow
- What is Deep Learning?
- Why Deep Learning is Popular?
- Which Book on Neural Networks and Deep Learning Should You Choose?
- Conclusion

**Best Books on Neural Networks and Deep Learning**

In this article, I have listed the most suitable **Books on Neural Networks and Deep Learning** for you.

**1. Deep Learning (Adaptive Computation and Machine Learning series**

**Authors-** Ian Goodfellow, Yoshua Bengio, Aaron Courville.

**About Book**–

This book is known as the “**Bible” of Deep Learning**. The author **Ian Goodfellow is the godfather of Deep Learning**. That’s why this book is special for everyone who wants to learn the basics of Deep Learning.

This book is theoretical. This Deep Learning book is especially for those who want to learn the basics and theory part of Deep Learning.

This book begins with **Machine Learning Basics**, covers the mathematical and conceptual topics relevant to Deep Learning. This Deep Learning book covers **linear algebra, probability theory and information theory, numerical computation**.

After that, this book covers **Modern Deep Learning Algorithms and Techniques.** In that section, this book covers **deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. **

This Book also describes applications of Deep Learning. Such as **natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. **In the end, this Deep Learning book describes the current research trends.

**You Should Read this Book, if-**

- You are an undergraduate or graduate student, professor, or one who wanna learn the basics of Deep Learning.
- You don’t have basic knowledge of Deep Learning.
- Or if you wanna learn the theory behind Deep Learning.

**Where to Buy this Book?**

**You can buy on Amazon- Deep Learning (Adaptive Computation and Machine Learning series) **

### 2. **Deep Learning with Python **

**Authors-** Francois Chollet

**About Book-**

This book is specially written for **beginners and intermediate programmers**. This book attracts me with its **Keras implementation** for each technique.

After reading this book, you will become **Keras Expert **and you can apply deep learning to your projects. This book is written in clear and easy language. You can understand concepts easily.

**What’s inside the Book?**

- Deep learning from first principles
- You will learn to set up your
**own deep-learning environment.** - This book covers
**Image-classification models** - You will learn
**Deep learning for text and sequences** - This book also covers
**Neural style transfer, text generation, and image generation**.

**You Should Read this Book, if-**

**You Should Read this Book, if-**

- You have
**intermediate Python Skills with no previous experience with Keras, TensorFlow, or machine learning is required.** - You are interested in
**Keras Library**or you want to learn**Deep Learning by implementing.** - And if you want to learn quickly about how Deep Learning is used in
**computer vision, text, and sequence learning.**

**Where to Buy this Book?**

**You can buy on Amazon-Deep Learning with Python**

**3. Neural Networks and Deep Learning **

**Authors-** Charu C. Aggarwal

**About Book-**

This book covers both **classical and modern models in deep learning**. The primary focus is on the **theory and algorithms of deep learning**. To understand the full functionality of Deep Learning and neural networks, the theory is important.

This book covers all your questions related to Neural Networks. Like-

- Why do neural networks work?
- When do they work better than off-the-shelf machine-learning models?
- When is depth useful?
- Why is training neural networks so hard?
- What are the pitfalls?

This book also covers different applications of Deep Learning and Neural networks. This book is divided into **3 categories**–

- The basics of neural networks.
- Fundamentals of neural networks.
- Advanced topics in neural networks.

**You Should Read this Book, if-**

**You Should Read this Book, if-**

- You are a
**graduate student, researcher, and practitioner**. - Or if you are a
**teacher**, because in this book Numerous exercises are available along with a solution manual to aid in classroom teaching.

#### **Where to Buy this Book?**

** You can buy on Amazon- Neural Networks and Deep Learning.**

**4. Hands-On Deep Learning Algorithms with Python **

**Author-** Sudharsan Ravichandiran

**About Book-**

In this book, you will understand basic to **advanced deep learning algorithms**, the **mathematical principles behind them, and their practical applications**.

I personally love this book. And the reason is its simple and easy-to-understand language. This book explains the complex maths behind Deep Learning in a super-easy way.

This book will give you **in-depth knowledge of the Basic to Advance Deep Learning algorithm** with the mathematics behind each algorithm. Due to its simplicity, this book addicts you to learn the next chapter.

After reading this book-

- You will learn how to build a
**neural network from scratch.** - Along with that, you will learn the
**mathematics behind deep learning models**. - And you can implement popular
**Deep learning algorithms CNNs, RNNs, and others using Tensorflow.**

**You Should Read this Book, if-**

- You are a Beginner and don’t have any prior knowledge in Deep Learning.
- You wanna learn coding concepts.
- Or you want to learn deep learning from scratch.

#### **Where to Buy this Book?**

** You can buy on Amazon- Hands-On Deep Learning Algorithms with Python.**

### 5. **Deep Learning: A Practitioner’s Approach **

**Author- **Adam Gibson and Josh Patterson’s

**About Book-**

Most of the books, I discussed uses Python code. But this book uses **Java code and the DL4J library. **Why this book uses Java? Because Java is mostly used in Programming Language especially in Big Companies.

This book covers the **fundamentals of Machine Learning and Deep Learning**. After covering fundamentals, this book covers J**ava-based deep learning code examples using DL4J. **

**You Should Read this Book, if-**

- If you have a specific project where you need to use Java Programming language.
- You want to understand how to operate the DL4J library.

#### **Where to Buy this Book?**

** You can buy on Amazon- Deep Learning: A Practitioner’s Approach **

**6. ****Hands-On Machine Learning with Scikit–Learn and TensorFlow**

**Authors-** Aurélien Géron

**About Book**–

This book gives you a **hands-on approach to learning by doing**. It starts with the more **traditional ML approaches (the Scikit-learn part)** giving you a great deal of context and practical tools for solving all kinds of problems. This book has an excellent balance between theory/background and implementation.

This practical book shows you how even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.

This Book uses concrete examples, minimal theory, and two production-ready Python frameworks—**Scikit-Learn and TensorFlow**.

**What’s inside the Book?**

The first part of the book explains basic Machine Learning Algorithms. **Support Vector Machine, Decision, Trees, Random Forests, and many more**. In that book,** Scikit-learn examples** for each of the algorithms are included.

In the second part, deep learning concepts through the **TensorFlow library** are explained.

In this book, you will learn-

- Explore the
**machine learning landscape, particularly neural nets** - Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore s
**everal training models, including support vector machines, decision trees, random forests, and ensemble methods** - Use the TensorFlow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets

**You Should Read this Book, if-**

- You have basic programming knowledge and beginner in Machine Learning and wants to start with the basics of coding.
- You are interested in the popular scikit-learn machine learning library.

#### **Where to Buy this Book?**

** You can buy on Amazon- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow**

**7. ** **TensorFlow 1.x Deep Learning Cookbook **

**Author**– Antonio Gulli, Amita Kapoor

**About Book-**

This book is written in a **CookBook Style**. That means a** little theory and lots of code**. This deep learning book is entirely hands-on and is a great reference for **TensorFlow **users.

In this book, you will learn how to efficiently use **TensorFlow, Google’s open-source framework for deep learning. **

You will implement different deep learning networks such as **Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs)** with easy to follow independent recipes.

You will learn how to make **Keras as a backend with TensorFlow**. Along with that, you will learn with a **problem-solution approach**, how to implement different deep neural architectures to carry out complex tasks at work.

**You Should Read this Book, if-**

- You are interested in TensorFlow Library and likes CookBook Style reading.
- You have a basic knowledge of Deep Learning.

#### **Where to Buy this Book?**

** You can buy on Amazon- TensorFlow 1.x Deep Learning Cookbook.**

**8. Neural Networks for Pattern Recognition **

**Author-** Christopher M. Bishop

**About Book-**

This is the first comprehensive treatment of **feed-forward neural networks** from the perspective of **statistical pattern recognition. **

After introducing the basic concepts, the book examines techniques for **modeling probability density functions** and the properties and merits of the **multi-layer perceptron and radial basis function network models**.

This Book also covered various forms of **error functions, principal algorithms for error function minimalization, learning, and generalization in neural networks. **

It is designed as a text, with over **100 exercises**, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

**You Should Read this Book, if-**

- You want to dive deep into Pattern Recognition.

#### **Where to Buy this Book-**

** You can buy on Amazon- Neural Networks for Pattern Recognition **

### 9. ** The Hundred-Page Machine Learning Book **

**Author-** Andriy Burkov

**About Book-**

The “Hundred-Page Machine Learning Book” by Andriy Burkov, is, in my opinion, the best book for those working with **machine learning libraries** but who don’t have an understanding of the underlying science behind the libraries.

This book explains it in a very down-to-earth way. In this Book, some **math** is used, nothing too excessive, and should be easy for anyone with some mathematical experience to grasp.

It will be** useful to practitioners,** and for the reader who understands that this is the first 100 (or actually 150) pages you will read, not the last, provides a solid introduction to the field.”

#### **Where to Buy this Book?**

** You can buy on Amazon- The Hundred-Page Machine Learning Book **

**10. Practical Deep Learning for Cloud, Mobile, and Edge: Real-World AI & Computer-Vision Projects Using Python, Keras & TensorFlow **

**Author-** Anirudh Koul, Siddha Ganju, Meher Kasam

**About Book-**

Whether you’re a software engineer aspiring to enter the world of deep learning,** a veteran data scientist, or a hobbyist **with a simple dream of making the next viral AI app, you might have wondered where to begin.

This **step-by-step guide** teaches you how to build practical deep learning applications for the cloud, mobile, browsers, and edge devices using a hands-on approach.

In this book, you will learn-

**Train, tune, and deploy computer vision models with Keras, TensorFlow, Core ML, and TensorFlow Lite.**- Develop AI for a range of devices including
**Raspberry Pi, Jetson Nano, and Google Coral.** - Explore fun projects, from
**Silicon Valley’s Not Hotdog app to 40+ industry case studies**. - Simulate an
**autonomous car in a video game environment and build a miniature version with reinforcement learning.** - Use
**transfer learning to train models**in minutes. - Discover
**50+ practical tips for maximizing model accuracy and speed, debugging, and scaling to millions of users.**

#### **Where to Buy this Book?**

**You can buy on Amazon-Practical Deep Learning for Cloud, Mobile, and Edge.**

So, these are the **Top 10 Books on neural networks and deep learning**. Now, I would like to give a brief introduction to Deep Learning.

**What is Deep Learning?**

Deep Learning is the **subpart of Machine Learning**. It is more robust than machine Learning. Deep Learning works on an Artificial Neural Network. Artificial Neural Network contains three layers- **Input Layer, Hidden Layer, and Output Layer.**

There may be **n number **of layers in the Hidden Layer. The deeper the Hidden Layer, the more accurate the result. That’s why it is known as Deep Learning.

**Why Deep Learning is Popular?**

Some features make Deep Learning more robust than Machine Learning-

- Deep Learning performs well on Large datasets, but Machine Learning can’t.
- In Deep Learning, you don’t need to feed all features manually like in Machine Learning. Feeding features manually is very time-consuming. This feature makes Deep Learning more powerful.
- Deep Learning can easily solve complex real-world problems, but Machine Learning can’t.

Due to these features, Deep Learning is getting more popular nowadays. Most people are using Deep Learning over Machine Learning.

Now let’s see which Book is good to learn Deep Learning-

**Which Book on Neural Networks and Deep Learning** **Should You Choose?**

For learning Deep Learning, you need to learn the **theory part as well as the practical part**. If you only focus on the practical and implementation part, you will miss some important theories. That’s why the book which balances both the theoretical and practical parts, is the best book for you.

That’s all.

**Conclusion**

In this article, you have discovered the **Top 10 Books on neural networks and deep learning**. Have you Bought or Read anyone of these Books?. If yes then tell your experience in the comment section.

I hope these **Top 10 Books on neural networks and deep learning** will help you to begin your Learning Journey.

All The Best.

## Learn the Basics of Deep Learning Here

**You May Also be Interested In**

**How Good is Udacity Deep Learning Nanodegree in 2024?****10 Best Books on Neural Networks and Deep Learning, You Should Read****Best Deep Learning Courses on Coursera You Need to Know in 2024****Deep Learning vs Neural Network, The Main Differences!What is Generative Adversarial Network? All You Need to Know**

**Top 5 Deep Learning Algorithms List, You Need to Know**

**What is Convolutional Neural Network? Super Easy Explanation!**

**Top 6 Skills Required for Deep Learning That Will Make You Expert!**

**Stochastic Gradient Descent- A Super Easy Complete Guide!**

**Gradient Descent Neural Network- Quick and Super Easy Explanation!**

**How does Neural Network Work? A step-by-step guide.**

**Activation Function and Its Types-Which one is Better?**

**Artificial Neural Network: What is Neuron? Ultimate Guide.**

**What is Deep Learning and Why it is Popular?**

Thank YOU!

Though of the Day…

– Henry Ford

‘Anyone who stops learning is old, whether at twenty or eighty. Anyone who keeps learning stays young.

### Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.