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Best Deep Learning books

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Best Deep Learning books for Beginners
Best deep learning books

Here are the best Deep Learning Books for all the enthuastics. we have listed the best Deep Leanring books for beginners to expert level based on the reviews from the previous readers, concepts etc. You can find the best Deep learning book from the list that is best suitable for you.

Best Deep Learning Books for beginners to advanced

Deep Learning with Python:

Deep Learning with Python has taught thousands of readers how to put the full capabilities of deep learning into action. This extensively revised full-colour second edition introduces deep learning using Python and Keras and is loaded with insights for both novice and experienced ML practitioners.

You’ll learn practical techniques that are easy to apply in the real world and important theories for perfecting neural networks.

In this revised and expanded new edition, Keras creator François Chollet offers insights for both novice and experienced machine learning practitioners.

As you move through this book, you’ll build your understanding through intuitive explanations, crisp color illustrations, and clear examples. You’ll quickly pick up the skills you need to start developing deep-learning applications.

Rating: 4.9

Pros:

  • This Deep learning Book is very useful for all the aspiring Data Scientists
  •  Useful to people who lack both programming and linear algebra.
  • A good book for learning and understanding how to use python for Deep Learning

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Learning Deep Learning Theory and Practice of Neural Networks, Computer Vision, NLP, and Transformers using Tensorflow:

Learning Deep Learning is a complete guide to DL. Illuminating both the core concepts and the hands-on programming techniques needed to succeed, this book is ideal for developers, data scientists, analysts, and others–including those with no prior machine learning or statistics experience.

After introducing the essential building blocks of deep neural networks, such as artificial neurons and fully connected, convolutional, and recurrent layers, Magnus Ekman shows how to use them to build advanced architectures, including the Transformer.

He describes how these concepts are used to build modern networks for computer vision and natural language processing Courses (NLP), including Mask R-CNN, GPT, and BERT. And he explains how a natural language translator and a system generate natural language descriptions of images.

The book thereby covers the two dominating Python libraries for DL used in industry and academia. He concludes with an introduction to neural architecture search (NAS), exploring important ethical issues and providing resources for further learning.

  • Explore and master core concepts: perceptrons, gradient-based learning, sigmoid neurons, and back propagation
  • See how DL frameworks make it easier to develop more complicated and useful neural networks
  • Discover how convolutional neural networks (CNNs) revolutionize image classification and analysis
  • Apply recurrent neural networks (RNNs) and long short-term memory (LSTM) to text and other variable-length sequences
  • Master NLP with sequence-to-sequence networks and the Transformer architecture
  • Build applications for natural language translation and image captioning

Rating: 4.6

Pros:

  • Highly recommended from novice to experts, teaches to code DL with enough theoretical background.
  • easier to read and understand than almost all if not all of the more recent texts on Deep Learning
  • You will learn all the mathematics and detailed analysis of the algorithms.
  • This book is great for AI practitioners because it presents the recent development of Deep Learning techniques with a good amount of codes.

Cons:

  • Before reading this book one should know the basics of ml and ai.

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Deep Learning for Vision Systems:

Computer vision is central to many leading-edge innovations, including self-driving cars, drones, augmented reality, facial recognition, and much, much more. Amazing new computer vision applications are developed every day, thanks to rapid advances in AI and deep learning (DL).

Deep Learning for Vision Systems teaches you the concepts and tools for building intelligent, scalable computer vision systems that can identify and react to objects in images, videos, and real life.

With author Mohamed Elgendy’s expert instruction and illustration of real-world projects, you’ll finally grok state-of-the-art deep learning techniques, so you can build, contribute to, and lead in the exciting realm of computer vision!

You will learn:

  • Image classification and object detection
  • Advanced deep learning architectures
  • Transfer learning and generative adversarial networks
  • DeepDream and neural style transfer
  • Visual embeddings and image search

Rating: 4.9

Pros:

  • Best book on Modern Computer Vision
  • It covers all the basics you need to know to build a deep learning model in the CV domain while at the same demystifying the complex things.
  • has great illustrations, flow, depth and does a beautiful job explaining ML for machine vision.
  • This book clearly demonstrates how a deep learning neural network works by using simple math calculations and graphs.
  • The book is well written and is perfect for approaching for the first time deep neural networks applied to computer vision and understanding the theory behind it.

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Deep learning: A Comprehensive Guide to Python Coding and Programming Machine Learning and neural networks for Data Analysis:

This book will not only teach you everything you should know about Python, but it will also give YOU a greater insight into coding, programming, machine learning, and data analysis.

Here is what this book will teach you:

  • The fundamental skills that will bootstrap YOU as a programmer
  • Everything you should know about coding, programming, machine learning, and data analysis
  • How to become an expert in using Python
  • Make your program come to life with practical exercises
  • How to start profiting from your coding and programming skills

This guide won’t only give you theoretical knowledge about Python and coding. It also contains so many small exercises that will help you start your journey of building a program. And, it is this journey that will teach you the most.

Deep learning is a type of machine learning that allows computers to learn from their mistakes and comprehend the world as a hierarchy of concepts. 

A graph of the hierarchies would be many layers deep, allowing the machine to understand sophisticated concepts by building them out of simpler ones.

Yes, you can learn a lot from reading about Python. But, the best way to learn is by using Python to build something. While you do that, you are quite likely to come across a few obstacles. But don’t worry! This book will help you conquer each and every one of them.

Rating: 4.5

Pros:

  • Easy to Understand
  • The book goes into detail on different types of networks and learning how to use Python for programming.
  • It also had exercises to help you get more familiar.
  • Everything you need to know about coding, data analysis and programming is included in the book.
  • Complete understanding of Deep Learning vs Machine Learning.

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Deep Learning for Coders with Fastai and PyTorch: AI Applications without a PhD:

Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code.

How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.

Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

If you are a complete beginner to deep learning & machine learning, you are most welcome here. Our only expectation is that you already know how to code, preferably in Python. If you are already a confident deep learning practitioner, you will also find a lot here.

In this book, we will be showing you how to achieve world-class results, including techniques from the latest research. As we will show, this doesn’t require advanced mathematical training or years of study. It just requires a bit of common sense and tenacity.

  • Train models in computer vision, natural language processing, tabular data, and collaborative filtering
  • Learn the latest deep learning techniques that matter most in practice
  • Improve accuracy, speed, and reliability by understanding how deep learning models work
  • Discover how to turn your models into web applications
  • Implement deep learning algorithms from scratch
  • Consider the ethical implications of your work
  • Gain insight from the foreword by PyTorch cofounder, Soumith Chintala.

Rating: 4.8

Pros:

  • The book is well written, well-thought-out and the ideas explored are interesting in and of themselves.
  •  It’s the best book for rapidly getting started and DOING real deep learning with your own data.

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Deep Learning: A Visual Approach:

Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest-growing field in artificial intelligence (AI).

Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books – the possibilities are endless.

Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals.

If you want to know how these tools work, and use them yourself, the answers are all within these pages.

And, if you’re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.

The book’s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including:

    How text generators create novel stories and articles

    How deep learning systems learn to play and win at human games

    How image classification systems identify objects or people in a photo

    How to think about probabilities in a way that’s useful to everyday life

    How to use the machine learning techniques that form the core of modern AI

Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It’s the future of AI, and this book allows you to fully envision it.

Rating: 4.8

Pros:

  • This book is jam-packed with insights, visuals, and clear explanations.
  • This book does a great job of presenting all the core ideas in a way that makes them clear and memorable.
  • Three “bonus chapters” provide an equally lucid introduction to the nuts and bolts of programming deep learning systems.

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Practical Deep Learning for Cloud, Mobile and Edge: Real-world AI & Computer Vision Projects using Python Keras & Tensorflow:

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. If your goal is to build something creative, useful, scalable, or just plain cool, this book is for you.

Relying on decades of combined industry experience transforming deep learning research into award-winning applications, Anirudh Koul, Siddha Ganju, and Meher Kasam guide you through the process of converting an idea into something that people in the real world can use.

  • 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.

Rating: 4.6

Pros:

  • It teaches well with examples that play directly into my scenarios on iOS and Android.
  • the intimidation factor of difficult concepts like Deep Neural Networks become a little bit more human.
  • The clear syntax, understandable language, and engaging examples support an excellent entrance into fields of Deep Learning, Computer Vision, and AI by not only providing easily accessible opportunities for hands-on learning, but also explaining the importance of the book’s content.

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Deep learning Systems: Algorithms, Compilers, and Processors for Large scale production:

This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications.

The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing.

Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications.

Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency.

Advancing DL systems generally involves three types of engineers:

  • data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists;
  • hardware designers that develop specialized hardware to accelerate the components in the DL models;
  • performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. 

Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets.

The purpose of this book is to provide a solid understanding of

  • the design, training, and applications of DL algorithms in industry;
  • the compiler techniques to map deep learning code to hardware targets;
  • the critical hardware features that accelerate DL systems. 

This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack.

The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today’s and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets.

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Deep Learning in Bioinformatics: Techniques and Applications in practice

Deep Learning in Bioinformatics: Techniques and Applications in Practice introduces the topic in an easy-to-understand way, exploring how it can be utilized for addressing important problems in bioinformatics, including drug discovery, de novo molecular design, sequence analysis, protein structure prediction, gene expression regulation, protein classification, biomedical image processing and diagnosis, biomolecule interaction prediction, and in systems biology.

The book also presents theoretical and practical successes of deep learning in bioinformatics, pointing out problems and suggesting future research directions

Dr. Izadkhah provides valuable insights and will help researchers use deep learning techniques in their biological and bioinformatics studies.

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Synthetic Data for Deep Learning:

The book can also serve as an introduction to several other important subfields of machine learning that are seldom touched upon in other books. Machine learning as a discipline would not be possible without the inner workings of optimization at hand.

The book includes the necessary sinews of optimization though the crux of the discussion centers on the increasingly popular tool for training deep learning models, namely synthetic data.

It is expected that the field of synthetic data will undergo exponential growth in the near future. This book serves as a comprehensive survey of the field.  

In the simplest case, synthetic data refers to computer-generated graphics used to train computer vision models. There are many more facets of synthetic data to consider.

In the section on basic computer vision, the book discusses fundamental computer vision problems, both low-level (e.g., optical flow estimation) and high-level (e.g., object detection and semantic segmentation), synthetic environments and datasets for outdoor and urban scenes (autonomous driving), indoor scenes (indoor navigation), aerial navigation, and simulation environments for robotics.

Additionally, it touches upon applications of synthetic data outside computer vision (in neural programming, bioinformatics, NLP, and more). It also surveys the work on improving synthetic data development and alternative ways to produce it such as GANs.

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Geometry of Deep Learning: A single Processing Perspective:

The focus of this book is on providing students with insights into geometry that can help them understand deep learning from a unified perspective.

Rather than describing deep learning as an implementation technique, as is usually the case in many existing deep learning books, here, deep learning is explained as an ultimate form of signal processing techniques that can be imagined. 

detailed explanation of the basic building blocks of deep neural networks from a biological and algorithmic point of view, the latest tools such as attention, normalization, Transformer, BERT, GPT-3, and others are described.

Here, too, the focus is on the fact that in these heuristic approaches, there is an important, beautiful geometric structure behind the intuition that enables a systematic understanding. A unified geometric analysis to understand the working mechanism of deep learning from high-dimensional geometry is offered.

Then, different forms of generative models like GAN, VAE, normalizing flows, optimal transport, and so on are described from a unified geometric perspective, showing that they actually come from statistical distance-minimization problems.

Because this book contains up-to-date information from both a practical and theoretical point of view, it can be used as an advanced deep learning textbook in universities or as a reference source for researchers interested in acquiring the latest deep learning algorithms and their underlying principles. 

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Conclusion:

These are the Best Deep Learning books that are listed. If you find any of the Best Deep learning Books are missing please comment in comment section. So, that we can update it soon. Thanks for reading.

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