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Deep Learning with TensorFlow 2 and Keras: Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API, 2nd Edition
Build machine and deep learning systems with the newly released TensorFlow 2 and Keras for the lab, production, and mobile devices
Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook DescriptionDeep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is forThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.
Table of ContentsNeural Network Foundations with TensorFlow 2.0TensorFlow 1.x and 2.xRegressionConvolutional Neural NetworksAdvanced Convolutional Neural NetworksGenerative Adversarial NetworksWord EmbeddingsRecurrent Neural NetworksAutoencodersUnsupervised LearningReinforcement LearningTensorFlow and CloudTensorFlow for Mobile and IoT and TensorFlow.jsAn introduction to AutoMLThe Math Behind Deep LearningTensor Processing Unit
Key FeaturesIntroduces and then uses TensorFlow 2 and Keras right from the startTeaches key machine and deep learning techniquesUnderstand the fundamentals of deep learning and machine learning through clear explanations and extensive code samplesBook DescriptionDeep Learning with TensorFlow 2 and Keras, Second Edition teaches neural networks and deep learning techniques alongside 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 is the machine learning library of choice for professional applications, while Keras offers a simple and powerful Python API for accessing TensorFlow. TensorFlow 2 provides full Keras integration, making advanced machine learning easier and more convenient than ever before.
This book also introduces neural networks with TensorFlow, runs through the main applications (regression, ConvNets (CNNs), GANs, RNNs, NLP), covers two working example apps, and then dives into TF in production, TF mobile, and using TensorFlow with AutoML.
What you will learnBuild machine learning and deep learning systems with TensorFlow 2 and the Keras APIUse Regression analysis, the most popular approach to machine learningUnderstand ConvNets (convolutional neural networks) and how they are essential for deep learning systems such as image classifiersUse GANs (generative adversarial networks) to create new data that fits with existing patternsDiscover RNNs (recurrent neural networks) that can process sequences of input intelligently, using one part of a sequence to correctly interpret anotherApply deep learning to natural human language and interpret natural language texts to produce an appropriate responseTrain your models on the cloud and put TF to work in real environmentsExplore how Google tools can automate simple ML workflows without the need for complex modelingWho this book is forThis book is for Python developers and data scientists who want to build machine learning and deep learning systems with TensorFlow. This book gives you the theory and practice required to use Keras, TensorFlow 2, and AutoML to build machine learning systems. Some knowledge of machine learning is expected.
Table of ContentsNeural Network Foundations with TensorFlow 2.0TensorFlow 1.x and 2.xRegressionConvolutional Neural NetworksAdvanced Convolutional Neural NetworksGenerative Adversarial NetworksWord EmbeddingsRecurrent Neural NetworksAutoencodersUnsupervised LearningReinforcement LearningTensorFlow and CloudTensorFlow for Mobile and IoT and TensorFlow.jsAn introduction to AutoMLThe Math Behind Deep LearningTensor Processing Unit
648 pages, Kindle Edition
Published December 27, 2019
42 people are currently reading
51 people want to read
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September 7, 2021
Great book
Excellent book for understanding state of the art deep learning models with great code examples. Definitely worth the time to explore in full
Excellent book for understanding state of the art deep learning models with great code examples. Definitely worth the time to explore in full
August 14, 2022
The book is well written. The Language is lucid and easy to understand. Can't wait to read the other books by the same author
September 16, 2024
relevant, with good theory and code, highly recommended if you are interested in the topic.
Displaying 1 - 3 of 3 reviews