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What is Deep Learning?
One may classify deep learning as a subclass of machine learning. It’s a study built on self-improvement through the analysis of computer algorithms. While machine learning relies on more basic ideas, deep learning uses artificial networks that mimic how people learn and reason.
Examples of Deep Learning at Work
Deep learning applications are employed in various fields, including automated vehicles and medical equipment. Here are a few examples proving deep learning’s vast implementation across varying industries.
1. Automated Driving
Automobile industry researchers automatically use deep learning to recognize items like stop signals and traffic lights. Deep learning is also used to identify pedestrians, which reduces accidents.
2. Aerospace and Defense
Deep learning is employed to distinguish between safe and risky places for troops and to recognize items from satellites that detect points of interest.
3. Industrial Automation
Deep learning can automatically determine when people or things are too close to heavy machinery, which helps to increase worker safety around it.
4. Electronics
Automatic speech and hearing translation use deep learning. Deep learning software, for instance, is used to power voice-activated home help systems that remember your preferences.
5. Medical Research
Medical researchers are using deep learning to identify cancer cells automatically. Researchers utilize a high-dimensional data collection produced by a sophisticated microscope created by UCLA research teams to teach deep learning programs to help recognize cancer cells accurately.
Ten Deep Learning Books That You Can Read
Now that we have a general idea of deep learning and its uses, let us look at the top 10 best deep learning books that everyone should read regardless of experience level.
1. Python Machine Learning
Author: Sebastian Raschka and Vahid Mirjalili
This deep learning textbook is likely one of the few focusing exclusively on Python. It helps you comprehend and create a variety of deep learning, data analysis, and machine learning techniques. It discusses several effective libraries, including Scikit-Learn, for developing various algorithms for machine learning. Then, the Tensor Flow module instructs you on deep learning.
Finally, it demonstrates the many data analysis possibilities that can be achieved using deep and machine learning. Also, it teaches you numerous techniques you can utilize to increase the quality of the model you create.
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2. Deep Learning from Scratch: Building with Python from First Principles
Author: Seth Weidman
This book is ideal for you if you’re a machine learning experienced data scientist who wants to go into deep learning. Deep Learning from Scratch offers a thorough introduction to deep learning for data scientists and other information specialists who are already knowledgeable about machine learning.
The book quickly transitions from core deep learning ideas to more complex concepts, algorithms, and neural network structures. The book is undoubtedly difficult to read because it guides the reader through mathematical, conceptual, and theoretical explanations, but the outcomes are all the more satisfying. The well-known deep learning platform PyTorch is used in multiple examples throughout the book.
3. Deep Learning for Coders with Fastai and PyTorch
Author: Jeremy Howard and Sylvain Gugger
A fast-developing topic, deep learning is also becoming more widely available. One no longer needs a PhD in mathematics or computer science to work with deep learning because of the advancement of accessible, user-friendly libraries and interfaces.
Fastai is one of these technologies; it was the first library to offer a unified interface for the most popular deep learning applications. A practical manual for creating models for deep learning with no prior knowledge of mathematics, little data, and little code is available as Deep Learning for Coders with Fastai and PyTorch. Along with coding methods, the book also discusses deep learning theory to aid you in understanding the principles of how neural networks function.
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4. Neural Networks and Deep Learning
Author: Michael Nielsen
This deep learning textbook can be read online and helps connect deep learning and neural networks. Neural networks are a programming paradigm that is influenced by the human brain. Neural Networks and Deep Learning, regarded as one of the greatest books on neural networks, takes a comprehensive approach to the topic to show how it may help solve typical issues with speech and image recognition and processing natural language.
While the book explains the complex mathematics underlying these deep learning ideas, Nielsen also offers explanations for each chapter’s summaries that do not include arithmetic. This is a fantastic resource for those new to deep learning because it is free and well-regarded.
5. Hands-On Deep Learning Algorithms with Python
Author: Sudharsan Ravichandran
This book improves your understanding of deep learning algorithms at all levels and shows you how to use the TensorFlow library to put them into practice. It is not advised for total beginners to read this book. To understand this book, you must have a solid understanding of Python and the fundamentals of machine learning.
It addresses the following subjects:
- The underlying mathematics of deep learning
- Brief description of the gradient descent variables AdaDelta, Nadam, and AMSGrad.
- A general idea of generative adversarial networks such as StackGAN, CycleGAN, and CGAN.
6. Deep Learning (Adaptive Computation and Machine Learning Series)
Author: Ian Goodfellow, Yoshua Bengio, and Aaron Courville
This educational handbook for college-level students is one of the best deep learning books and covers deep learning principles, and is meant to assist those just starting in the field. The writers, who are experts in their field, emphasize the value of studying mathematical ideas like probability and linear algebra to comprehend deep learning beyond just coding. Speech recognition and sequence modeling are two other deep learning principles covered in this book that are frequently employed in the business.
7. Deep Learning with Python
Author: François Chollet
When it was first released in 2017, Deep Learning with Python quickly achieved bestseller status and gained a reputation for being one of the best deep learning books of 2017. The October 2021 edition is jam-packed with new information and useful tips. The developer of the well-known deep learning system Keras, François Chollet, covers the subject with tips for both novice and experienced machine learning practitioners.
The second iteration of Deep Learning with Python has a pleasant and straightforward style and additional information that considers recent advancements in the field. Insightful explanations, colorful visual representations, and coding examples using TensorFlow, Keras, and Python, are spread all through the book to give you everything users need to start with deep learning.
8. Fundamentals of Deep Learning: Designing Next-Generation Machine Learning Algorithms
Author: Nithin Buduma, Nikhil Buduma, and Joe Papa
Deep learning rose to the top of artificial intelligence when neural networks began producing amazing results in the 2010s. Deep learning is a current academic topic that powers some of the most creative data science applications. Due to its complexity, deep learning is frequently thought of as a field only open to experts with PhD-level mathematics and machine learning expertise. The opposite is proved in Fundamentals of Deep Learning.
This deep learning textbook seeks to introduce the field without using complicated terminology. The book will walk you through various popular deep learning use cases, from analyzing text and images to creating reinforcement learning methods, all based on useful, Python-based code examples.
9. The Hundred-Page Machine Learning Book
Author: Andriy Burkov
An all-inclusive, simple-to-read book that covers machine learning and deep learning concepts. You will have a sufficient understanding of various machine learning and deep learning model types and their applications after reading “The Hundred-Page Machine Learning Book.” However, it only provides a basic explanation of the idea; hence, those just starting should search elsewhere for a deeper grasp.
Topics covered in this deep learning textbook:
- Supervised versus unsupervised learning: theory and distinctions
- Analysis of Neural clusters and networks
10. Grokking Deep Reinforcement Learning
Author: Miguel Morales
Deep reinforcement learning is a subset of deep learning (DRL). This subject relies on user experience to identify the most practical and efficient solutions for a specific method or issue. A few of the most sophisticated DRL apps can even defeat grandmasters in chess and forecast stock market moves.
By offering tasks to help readers create their deep learning platforms using Python, Morales’ book takes a practical approach to teaching DRL. It also uses illustrations and examples to emphasize DRL’s basic ideas and real-world uses. To ensure that you always have accessibility on your mobile or desktop device, Grokking Deep Reinforcement Learning also features a companion booklet.
Conclusion
Compared to AI and ML, deep learning is a fairly young idea or technology. However, it has just as much, if not more, potential to transform organizations and businesses in the years to come. There’s global confidence that deep learning will significantly alter people’s lives in various ways, from more sophisticated and lifelike personal assistants to fully autonomous autos.
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