Learning TensorFlow.js<\/h3>\n
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Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde (Google Developer Expert in machine learning and the web) provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.<\/p>\n
You’ll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you’ll know how to build and deploy production-readydeep learning systems with TensorFlow.js.<\/p>\n
Explore tensors, the most fundamental structure of machine learning
\nConvert data into tensors and back with a real-world example
\nCombine AI with the web using TensorFlow.js
\nUse resources to convert, train, and manage machine learning data
\nBuild and train your own training models from scratch<\/p>\n
This is the best Tensorflow book for Beginners in 2026<\/strong>.<\/p>\n No products found.<\/p>\n This easy-to-use reference for TensorFlow 2 design patterns in Python will help you make informed decisions for various use cases. Author KC Tung addresses common topics and tasks in enterprise data science and machine learning practices rather than focusing on TensorFlow itself.<\/p>\n When and why would you feed training data as using NumPy or a streaming dataset? How would you set up cross-validations in the training process? How do you leverage a pretrained model using transfer learning? How do you perform hyperparameter tuning? Pick up this pocket reference and reduce the time you spend searching through options for your TensorFlow use cases.<\/p>\n Understand best practices in TensorFlow model patterns and ML workflows No products found.<\/p>\n Unlock the TensorFlow design secrets behind successful deep learning applications! Deep learning StackOverflow contributor Thushan Ganegedara teaches you the new features of TensorFlow 2 in this hands-on guide. You will learn:<\/p>\n Fundamentals of TensorFlow .<\/p>\n No products found.<\/p>\n 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 bestselling 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.<\/p>\n With this updated third edition, author Aurélien Géron 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.<\/p>\n Use Scikit-learn to track an example ML project end to end This is one of the best TensorFlow books<\/strong> in 2026.<\/p>\n \n 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.<\/p>\n This book also shows you how to create neural networks with TensorFlow, runs through popular algorithms (regression, convolutional neural networks (CNNs), transformers, generative adversarial networks (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 Advanced Deep Learning with TensorFlow 2 and Keras by Rowel Atienza is a fully updated edition of the Guide to Successful Advanced Deep Learning Techniques available today. Revised for TensorFlow 2.x, this edition introduces you to the practical side of deep learning with new chapters on unsupervised learning using mutual information, object detection (SSD), and semantic segmentation (FCN and PSPNet), allowing you to create your own own cut. cutting edge artificial intelligence projects.Using Keras as an open source deep learning<\/a> library, the book features hands-on projects that show you how to create more efficient AI with the latest techniques.<\/p>\n Starting with an overview of multilayer perceptrons (MLP), convolutional neural networks (CNN), and recurrent neural networks (RNN), the book introduces more cutting-edge techniques as you explore neural network architecture programs, including ResNet and DenseNet, and how to create automatic encoders. Next, you will learn about GANs and how they can unlock new levels of AI performance. Next, you will discover how a Variational Autoencoder (VAE) is implemented and how GANs and VAEs have the generating power to synthesize data that can be extremely attractive to humans. You will also learn how to implement DRLs such as Deep Q-Learning and Policy Gradient Methods, which are essential for many modern results in AI.<\/p>\n No products found.<\/p>\n Learning TensorFlow by Tom Hope, Yehezkel S. Resheff and Itay Lieder gives a hands-on approach to TensorFlow fundamentals. Inspired by the human brain, deep neural networks made up of huge amounts of data can solve complex tasks with unprecedented precision. This book provides an end-to-end guide to TensorFlow’s top open source software library that helps you build and train computer perspectives, automated natural language processing (NLP), neural networks for recognition, vocal and general predictive analysis.<\/p>\n Authors Tom Hope, Ezekiel Risheff, and Itte have proposed a hands-on approach to the basics of tensorflow to a wide range of technological audiences, from scientists and data engineers to students and researchers. Before delving further into topics such as neural network architecture, tensorboard visualization, tensorflow abstraction libraries, and multithreaded input pipelines, you will begin to study some basic examples in tensorflow. By the end of this book, you will know how to create and set up a production-ready deep learning system in TensorFlow.<\/p>\nTensorFlow 2 Pocket Reference: Building and Deploying Machine Learning Models<\/h3>\n
\n Use code snippets as templates in building TensorFlow models and workflows
\n Save development time by integrating prebuilt models in TensorFlow Hub
\n Make informed design choices about data ingestion, training paradigms, model saving, and inferencing
\n Address common scenarios such as model design style, data ingestion workflow, model training, and tuning <\/p>\nTensorFlow in Action<\/h3>\n
\n Implementing deep learning networks
\n Picking a high-level Keras API for model building with confidence
\n Writing comprehensive end-to-end data pipelines
\n Building models for computer vision and natural language processing
\n Utilizing pretrained NLP models
\n Recent algorithms including transformers, attention models, and ElMo<\/p>\nHands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow 3rd Edition<\/h3>\n
\n Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
\n Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
\n Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
\n Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning <\/p>\nDeep Learning with TensorFlow 2 and Keras 3rd Edition<\/h3>\n
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\nby Antonio Gulli, Amita Kapoor, Sujit Pal. 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.<\/p>\n
\nWhat you will learn<\/p>\n
\n Discover the world of transformers, from pretraining to fine-tuning to evaluating them
\n Apply self-supervised learning to natural language processing, computer vision, and audio signal processing
\n Combine probabilistic and deep learning models using TensorFlow Probability
\n Train your models on the cloud and put TF to work in real environments
\n Build machine learning and deep learning systems with TensorFlow 2.x and the Keras API<\/p>\nAdvanced Deep Learning with TensorFlow 2 and Keras 2nd Edition<\/h3>\n\n
![\"Advanced](\"https:\/\/m.media-amazon.com\/images\/I\/51hFJ3ADGvL._SL160_._SL160_.jpg\")
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Machine Learning with TensorFlow<\/h3>\n\n
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