One of the most popular data science topics right now is deep learning. Deep learning, a branch of machine learning, is concerned with strong algorithms known as neural networks that are modelled after the architecture and operations of the human brain. It has been the main driver of artificial intelligence advancements in recent years. Deep Learning Books can help you to know more about this topic.
Autonomous vehicles, chatbots, image recognition, robotics, and other cutting-edge applications are powered by neural networks. Acquiring knowledge of neural network theory and practice is quickly turning into a must for data scientists and other data professionals. Deep learning, however, is a challenging subject that takes willpower and a certain level of abstraction to understand.
Popular Deep Learning Books
1. Deep Learning with Python by François Chollet
Deep Learning with Python quickly became one of the best-selling Deep Learning Books after its release, and its recent update is brimming with more knowledge and useful tips. The well-known deep learning framework Keras’ author, François Chollet, examines the area of deep learning with knowledge that will benefit both novice and seasoned machine learning professionals.
Deep Learning with Python, second edition, has an easy-to-read design and fresh revisions that take into account the latest advancements in the area. This book contains all the information you need to get started with deep learning, including color pictures, logical explanations, and code examples using Python, Keras, and TensorFlow.
Available on: Amazon, AbeBooks.
Price:
- Audiobook: Free with Audible Premium.
- Kindle edition: $43.99.
- Paperback: $53.46.
2. Grokking Deep Reinforcement Learning by Miguel Morales
There is a subset of deep reinforcement learning (DRL) inside deep learning. The goal of this discipline inside a discipline is to identify the most practical and efficient solutions for a specific process or issue via user experience and trial and error.
With activities that guide readers through the process of creating their deep learning systems using the Python programming language, Morales’ book takes a hands-on approach to teaching DRL. To emphasize the essential ideas and real-world uses of DRL, it also makes extensive use of examples and illustrations.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $36.99.
- Paperback: $44.99.
3. Deep Learning for Vision Systems by Mohamed Elgendy
Self-driving cars, however still seem very futuristic, are not too far off, and will soon be standard. Many modern automobiles even now come equipped with sensors that identify things in your path as you’re reversing out of the driveway.
The main technological advancement facilitating this progress is computer vision, made possible by deep learning, which provides computers with “eyes” to enable them to engage with the physical world. The vice president of engineering at Rakuten and AI specialist Mohamed Elgendy has written a deep learning book that is endlessly accessible and packed with knowledge.
This one of the Top Deep Learning Books provides tutorials to assist in developing face recognition and picture-generating applications. It employs elementary algebra to teach the ideas that let computers “see” and learn about the real world.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $36.99.
- Paperback: $27.98.
4. Fundamentals of Deep Learning: Designing Next-Generation Machine Learning Algorithms by Nithin Buduma, Nikhil Buduma and Joe Papa
Deep learning emerged as the cutting edge of artificial intelligence in the 2010s when neural networks began to show remarkable outcomes. Deep learning is a dynamic field of study that powers some of the most cutting-edge data science applications available today.
The goal of the book is to introduce the field without using technical terms. The book will walk you through some of the most popular use cases of deep learning, from processing photos and text analysis to creating reinforcement learning models, all based on real-world, Python-based code examples.
Available on: Amazon, Bookshop.
Price:
- Kindle edition: $34.07.
- Paperback: $34.
5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This one of the popular Deep Learning Books for college students covers the foundations of deep learning and is meant to assist people who are entirely new to the area.
The writers, who are experts in their domain, emphasize how crucial it is to master mathematical ideas. Such as probability and linear algebra to comprehend deep learning in a deeper way than merely coding. Sequence modelling and voice recognition are two deep learning principles that are often used in the field.
You may also get lectures, exercises, and connections to further websites via the URL (in the title, above). For those who are brand-new to the field, this is an excellent place to start.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $62.99.
- Paperback: $89.99.
- Hardcover: $92.40.
6. Deep Learning: A Practitioners Approach by Josh Patterson and Adam Gibson
Creating and implementing scalable, readily maintained models is one of the key issues businesses have with deep learning and machine learning. In an attempt to resolve this problem, Deep Learning: A Practitioner’s Approach is one of the most useful books on the topic. Deep learning theory, industrial best practices, and a few nonacademic application cases are all blended in this book.
The fact that the book contains code examples built in the open-source framework DL4J—the authors’ method for creating deep learning processes fit for production—is noteworthy. Because DL4J is dependent on the Java Virtual Machine, this book offers an excellent way to experiment with Java’s deep learning capabilities.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $38.49.
- Paperback: $59.99.
7. Deep Learning for Coders with Fastai and PyTorch by Jeremy Howard and Sylvain Gugger
In addition to being a quickly developing area, deep learning also becomes easier to access. Fastai, is the first library to provide a standardized user interface for the most popular deep-learning apps. With Fastai and PyTorch, Deep Learning for Coders offers a practical manual for creating deep learning models.
The book includes deep learning theory in addition to coding examples. It will help you comprehend the principles of neural network research.
Available on: Amazon, AbeBooks.
Price:
- Kindle edition: $34.73.
- Paperback: $36.56.
8. Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence by Jon Krohn, Grant Beyleveld, and Aglaé Bassens
There are a thousand words in a picture. That’s the premise behind this one of the Top Deep Learning Books, a special tool for understanding deep learning methods and applications via clear, visual aids.
The book simplifies the subject for a wider audience by abstracting away most of its intricacy and including vibrant visuals and character-focused storytelling. There is very little technical jargon, and practical Jupyter Notebooks containing Python code are used to accompany the applications. Some of the most well-known deep learning frameworks, such as TensorFlow, Keras, and PyTorch, are introduced in the book.
Available on: Amazon, WorldCat.
Price:
- Kindle edition: $29.99.
- Paperback: $39.62.
The Future of Deep Learning
While the usage of AI has been beneficial to organizations, a McKinsey study revealed that deep learning applications in business are still in their infancy. The developments of this AI method, however, seem quite promising.
Businesses need to analyze and comprehend the vast volumes of data, or “big data,” that they own. It is impossible to use traditional machine learning techniques, such as Naïve Bayes, support vector machines, and decision trees, directly to raw data.
To categorize the data, it is usually essential to perform a preprocessing step called feature extraction to transform the data into a format that algorithms can comprehend. It’s a lengthy and intricate procedure.
Deep learning does away with the need for human feature extraction as neural networks. It can automatically extract features from unprocessed data. You must read some Deep Learning Books if you want to be a part of a glorious future.
FAQ
Q: What is the minimum memory demand for deep learning?
A: For entry-level deep-learning models, a GPU memory of 4GB will suffice; an 8GB GPU is suggested, but it will come at a high cost.
Q: Which library is most suitable for deep learning study?
A: Keras. Built on top of TensorFlow is a high-level machine learning library called Keras. It is intended to make deep learning model construction and training simple. Neural networks and other machine learning models may be constructed using a variety of layers and models offered by Keras.
Q: Which language is most suitable for deep learning?
A: Python’s simplicity and adaptability make it one of the best options for machine learning. It offers an extensive ecosystem of frameworks and tools, such as PyTorch and TensorFlow, that make it easier to develop sophisticated machine learning models.