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Best Keras Books for Beginners to Advanced to know in 2022

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Best Keras Books for Beginners to Advanced
Best Keras books for Beginners to Advanced

Are you looking for the best books on Keras?… If yes, then this article is for you. In this article, you will find the Best Keras Books for Beginners & advanced like Beginner courses, and Practice test coursesSo, check these Best Keras Books for Beginners and find the Best Keras Books for Beginners to Advanced according to your need.

In the previous article, I shared the Best Machine Learning Books for beginners to Advanced to read in 2022, you can go through the list and enjoy reading.

Best Keras Books for Beginners to Advanced to know in 2022

Advanced Deep Learning with TensorFlow 2 and Keras: Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

Using Keras as an open-source deep learning library, the book features hands-on projects that show you how to create more effective AI with the most up-to-date techniques.

Starting with an overview of multi-layer perceptrons (MLPs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), the book then introduces more cutting-edge techniques as you explore deep neural network architectures, including ResNet and DenseNet, and how to create autoencoders. You will then learn about GANs, and how they can unlock new levels of AI performance.

Next, you’ll discover how a variational autoencoder (VAE) is implemented, and how GANs and VAEs have the generative power to synthesize data that can be extremely convincing to humans. You’ll also learn to implement DRL such as Deep Q-Learning and Policy Gradient Methods, which are critical to many modern results in AI.

  • Use mutual information maximization techniques to perform unsupervised learning
  • Use segmentation to identify the pixel-wise class of each object in an image
  • Identify both the bounding box and class of objects in an image using object detection
  • Learn the building blocks for advanced techniques – MLPss, CNN, and RNNs
  • Understand deep neural networks – including ResNet and DenseNet
  • Understand and build autoregressive models – autoencoders, VAEs, and GANs
  • Discover and implement deep reinforcement learning methods

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Neural Networks with Keras Cookbook: Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots

You will learn about how neural networks work and the impact of various hyper parameters on a network’s accuracy along with leveraging neural networks for structured and unstructured data.

You will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.

You will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.

Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.

By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.

  • Build multiple advanced neural network architectures from scratch
  • Explore transfer learning to perform object detection and classification
  • Build self-driving car applications using instance and semantic segmentation
  • Understand data encoding for image, text and recommender systems
  • Implement text analysis using sequence-to-sequence learning
  • Leverage a combination of CNN and RNN to perform end-to-end learning
  • Build agents to play games using deep Q-learning

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Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

Through a recent series of breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This best-selling book uses concrete examples, minimal theory, and production-ready Python frameworks–scikit-learn, Keras, and TensorFlow–to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.

With this updated third edition, author Aurelien Geron explores a range of techniques, starting with simple linear regression and progressing to deep neural networks. Numerous code examples and exercises throughout the book help you apply what you’ve learned. Programming experience is all you need to get started.

  • Use scikit-learn to track an example machine learning project end to end
  • Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
  • Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
  • Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, and transformers
  • Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
  • Train neural nets using multiple GPUs and deploy them at scale using Google’s Vertex AI

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Deep Learning with TensorFlow and Keras: Build and deploy supervised, unsupervised, deep, and reinforcement learning models

Deep Learning with TensorFlow and Keras, teaches you neural networks and deep learning techniques using TensorFlow (TF) and Keras. You’ll learn how to write deep learning applications in the most powerful, popular, and scalable machine learning stack available.

TensorFlow 2.x focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs based on Keras, and flexible model building on any platform. This book uses the latest TF 2.0 features and libraries to present an overview of supervised and unsupervised machine learning models and provides a comprehensive analysis of deep learning and reinforcement learning models using practical examples for the cloud, mobile, and large production environments.

This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, GANs, recurrent neural networks (RNNs), natural language processing (NLP), and Graph Neural Networks (GNNs)), covers working example apps, and then dives into TF in production, TF mobile, and TensorFlow with AutoML.

  • Learn how to use the popular GNNs with TensorFlow to carry out graph mining tasks
  • Discover the world of transformers, from pretraining to fine-tuning to evaluating them
  • Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
  • Combine probabilistic and deep learning models using TensorFlow Probability
  • Train your models on the cloud and put TF to work in real environments
  • Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API

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Applications of Deep Neural Networks with Keras

Deep learning is a group of exciting new technologies for neural networks. Through advanced training techniques and neural network architectural components, it is now possible to create neural networks that can handle tabular data, images, text, and audio as both input and output.

Deep learning allows a neural network to learn information hierarchies like the human brain’s function. This book will introduce the student to classic neural network structures, Convolution Neural Networks (CNN), Transformers, Long Short-Term Memory (LSTM), Gated Recurrent Neural Networks (GRU), General Adversarial Networks (GAN), and reinforcement learning.

This book covers the application of these architectures to computer vision, time series, security, natural language processing (NLP), and data generation.

The book presents both GPU and CPU processing for deep learning. The focus is primarily on applying deep learning to problems and introducing mathematical foundations as needed.

Students will use the Python programming language to implement deep learning using Google TensorFlow and Keras. Some applications make use of PyTorch.

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Natural Language Processing and Computational Linguistics: A practical guide to text analysis with Python, Gensim, spaCy, and Keras

This book shows you how to use natural language processing, and computational linguistics algorithms, to make inferences and gain insights about data you have. These algorithms are based on statistical machine learning and artificial intelligence techniques. The tools to work with these algorithms are available to you right now – with Python, and tools like Gensim and spaCy.

You’ll start by learning about data cleaning, and then how to perform computational linguistics from first concepts. You’re then ready to explore the more sophisticated areas of statistical NLP and deep learning using Python, with realistic language and text samples. You’ll learn to tag, parse, and model text using the best tools. You’ll gain hands-on knowledge of the best frameworks to use, and you’ll know when to choose a tool like Gensim for topic models, and when to work with Keras for deep learning.

This book balances theory and practical hands-on examples, so you can learn about and conduct your own natural language processing projects and computational linguistics. You’ll discover the rich ecosystem of Python tools you have available to conduct NLP – and enter the interesting world of modern text analysis.

  • Why text analysis is important in our modern age
  • Understand NLP terminology and get to know the Python tools and datasets
  • Learn how to pre-process and clean textual data
  • Convert textual data into vector space representations
  • Using spaCy to process text
  • Train your own NLP models for computational linguistics
  • Use statistical learning and Topic Modeling algorithms for text, using Gensim and scikit-learn
  • Employ deep learning techniques for text analysis using Keras

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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.

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Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability

Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability teaches the increasingly popular probabilistic approach to deep learning that allows you to refine your results more quickly and accurately without much trial-and-error testing. Emphasizing practical techniques that use the Python-based Tensorflow Probability Framework, you’ll learn to build highly-performant deep learning applications that can reliably handle the noise and uncertainty of real-world data.

Probabilistic Deep Learning is a hands-on guide to the principles that support neural networks. Learn to improve network performance with the right distribution for different data types, and discover Bayesian variants that can state their own uncertainty to increase accuracy. This book provides easy-to-apply code and uses popular frameworks to keep you focused on practical applications.

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And here the list ends. So, these are the Best Keras Books for Beginners to Advanced. I will keep adding more Best Books on Keras to this list.

Conclusion

I hope these Best Keras Books for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.

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