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TensorFlow in action /

In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts oth...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ganegedara, Thushan (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Shelter Island : Manning Publications, [2022]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Ganegedara, Thushan,  |e author. 
245 1 0 |a TensorFlow in action /  |c Thushan Ganegedara. 
264 1 |a Shelter Island :  |b Manning Publications,  |c [2022] 
300 |a 1 online resource (1 volume.) :  |b illustrations 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
500 |a Includes index. 
500 |a Covers TensorFlow version 2.9. 
520 |a In TensorFlow in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. Author Thushan Ganegedara uses quirky stories, practical examples, and behind-the-scenes explanations to demystify concepts otherwise trapped in dense academic papers. As you dive into modern deep learning techniques like transformer and attention models, you’ll benefit from the unique insights of a top StackOverflow contributor for deep learning and NLP. 
588 |a Description based on print version record. 
505 0 |a Intro -- TensorFlow in Action -- Copyright -- dedication -- contents -- front matter -- preface -- acknowledgments -- about this book -- Who should read this book? -- How this book is organized: A roadmap -- About the code -- liveBook discussion forum -- about the author -- about the cover illustration -- Part 1 Foundations of TensorFlow 2 and deep learning -- 1 The amazing world of TensorFlow -- 1.1 What is TensorFlow? -- 1.1.1 An overview of popular components of TensorFlow -- 1.1.2 Building and deploying a machine learning model -- 1.2 GPU vs. CPU -- 1.3 When and when not to use TensorFlow 
505 8 |a 1.3.1 When to use TensorFlow -- 1.3.2 When not to use TensorFlow -- 1.4 What will this book teach you? -- 1.4.1 TensorFlow fundamentals -- 1.4.2 Deep learning algorithms -- 1.4.3 Monitoring and optimization -- 1.5 Who is this book for? -- 1.6 Should we really care about Python and TensorFlow 2? -- Summary -- 2 TensorFlow 2 -- 2.1 First steps with TensorFlow 2 -- 2.1.1 How does TensorFlow operate under the hood? -- 2.2 TensorFlow building blocks -- 2.2.1 Understanding tf.Variable -- 2.2.2 Understanding tf.Tensor -- 2.2.3 Understanding tf.Operation 
505 8 |a 2.3 Neural network-related computations in TensorFlow -- 2.3.1 Matrix multiplication -- 2.3.2 Convolution operation -- 2.3.3 Pooling operation -- Summary -- Answers to exercises -- 3 Keras and data retrieval in TensorFlow 2 -- 3.1 Keras model-building APIs -- 3.1.1 Introducing the data set -- 3.1.2 The Sequential API -- 3.1.3 The functional API -- 3.1.4 The sub-classing API -- 3.2 Retrieving data for TensorFlow/Keras models -- 3.2.1 tf.data API -- 3.2.2 Keras DataGenerators -- 3.2.3 tensorflow-datasets package -- Summary -- Answers to exercises -- 4 Dipping toes in deep learning 
505 8 |a 4.1 Fully connected networks -- 4.1.1 Understanding the data -- 4.1.2 Autoencoder model -- 4.2 Convolutional neural networks -- 4.2.1 Understanding the data -- 4.2.2 Implementing the network -- 4.3 One step at a time: Recurrent neural networks (RNNs) -- 4.3.1 Understanding the data -- 4.3.2 Implementing the model -- 4.3.3 Predicting future CO2 values with the trained model -- Summary -- Answers to exercises -- 5 State-of-the-art in deep learning: Transformers -- 5.1 Representing text as numbers -- 5.2 Understanding the Transformer model -- 5.2.1 The encoder-decoder view of the Transformer 
505 8 |a 5.2.2 Diving deeper -- 5.2.3 Self-attention layer -- 5.2.4 Understanding self-attention using scalars -- 5.2.5 Self-attention as a cooking competition -- 5.2.6 Masked self-attention layers -- 5.2.7 Multi-head attention -- 5.2.8 Fully connected layer -- 5.2.9 Putting everything together -- Summary -- Answers to exercises -- Part 2 Look ma, no hands! Deep networks in the real world -- 6 Teaching machines to see: Image classification with CNNs -- 6.1 Putting the data under the microscope: Exploratory data analysis -- 6.1.1 The folder/file structure -- 6.1.2 Understanding the classes in the data set 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Machine learning. 
650 0 |a Artificial intelligence. 
650 2 |a Artificial Intelligence 
650 2 |a Machine Learning 
650 6 |a Apprentissage automatique. 
650 6 |a Intelligence artificielle. 
650 7 |a artificial intelligence.  |2 aat 
650 7 |a Artificial intelligence  |2 fast 
650 7 |a Machine learning  |2 fast 
776 0 8 |i Print version:  |a Ganegedara, Thushan.  |t Tensorflow 2.0 in action.  |d Shelter Island : Manning Publications, 2022  |z 9781617298349  |w (OCoLC)1289279869 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781617298349/?ar  |z Texto completo (Requiere registro previo con correo institucional) 
938 |a ProQuest Ebook Central  |b EBLB  |n EBL7114298 
938 |a EBSCOhost  |b EBSC  |n 3376799 
994 |a 92  |b IZTAP