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|a Bileschi, Stanley
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| 245 |
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|a Deep Learning with JavaScript
|c Bileschi, Stanley
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| 250 |
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|a 1st edition
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| 260 |
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|b Manning Publications
|c 2020
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| 300 |
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|a 560 pages
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| 505 |
0 |
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|a 13.4. Pointers for further exploration -- Final words -- Appendix A. Installing tfjs-node-gpu and its dependencies -- A.1. Installing tfjs-node-gpu on Linux -- A.2. Installing tfjs-node-gpu on Windows -- Appendix B.A quick tutorial of tensors and operations in TensorFlow.js -- B.1. Tensor creation and tensor axis conventions -- B.2. Basic tensor operations -- B.3. Memory management in TensorFlow.js: tf.dispose() and tf.tidy() -- B.4. Calculating gradients -- Exercises -- Glossary -- Index -- List of Figures -- List of Tables -- List of Listings
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|a 6.5. Data augmentation -- Exercises -- Summary -- Chapter 7. Visualizing data and models -- 7.1. Data visualization -- 7.2. Visualizing models after training -- Materials for further reading and exploration -- Exercises -- Summary -- Chapter 8. Underfitting, overfitting, and the universal workflow of machine learning -- 8.1. Formulation of the temperature-prediction problem -- 8.2. Underfitting, overfitting, and countermeasures -- 8.3. The universal workflow of machine learning -- Exercises -- Summary -- Chapter 9. Deep learning for sequences and text -- 9.1. Second attempt at weather prediction: Introducing RNNs -- 9.2. Building deep-learning models for text -- 9.3. Sequence-to-sequence tasks with attention mechanism -- Materials for further reading -- Exercises -- Summary -- Chapter 10. Generative deep learning -- 10.1. Generating text with LSTM -- 10.2. Variational autoencoders: Finding an efficient and structured vec- ctor representation of images -- 10.3. Image generation with GANs -- Materials for further reading -- Exercises -- Summary -- Chapter 11. Basics of deep reinforcement learning -- 11.1. The formulation of reinforcement-learning problems -- 11.2. Policy networks and policy gradients: The cart-pole example -- 11.3. Value networks and Q-learning: The snake game example -- Materials for further reading -- Exercises -- Summary -- Part 4. Summary and closing words -- Chapter 12. Testing, optimizing, and deploying models -- 12.1. Testing TensorFlow.js models -- 12.2. Model optimization -- 12.3. Deploying TensorFlow.js models on various platforms and environments -- Materials for further reading -- Exercises -- Summary -- Chapter 13. Summary, conclusions, and beyond -- 13.1. Key concepts in review -- 13.2. Quick overview of the deep-learning workflow and algorithms in TensorFlow.js -- 13.3. Trends in deep learning
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|a Intro -- Copyright -- Brief Table of Contents -- Table of Contents -- Foreword -- Preface -- Acknowledgments -- About this Book -- About the Authors -- About the cover illustration -- Part 1. Motivation and basic concepts -- Chapter 1. Deep learning and JavaScript -- 1.1. Artificial intelligence, machine learning, neural networks, and deep learning -- 1.2. Why combine JavaScript and machine learning? -- 1.3. Why TensorFlow.js? -- Exercises -- Summary -- Part 2. A gentle introduction to TensorFlow.js -- Chapter 2. Getting started: Simple linear regression in TensorFlow.js -- 2.1. Example 1: Predicting the duration of a download using TensorFlow.js -- 2.2. Inside Model.fit(): Dissecting gradient descent from example 1 -- 2.3. Linear regression with multiple input features -- 2.4. How to interpret your model -- Exercises -- Summary -- Chapter 3. Adding nonlinearity: Beyond weighted sums -- 3.1. Nonlinearity: What it is and what it is good for -- 3.2. Nonlinearity at output: Models for classification -- 3.3. Multiclass classification -- Exercises -- Summary -- Chapter 4. Recognizing images and sounds using convnets -- 4.1. From vectors to tensors: Representing images -- 4.2. Your first convnet -- 4.3. Beyond browsers: Training models faster using Node.js -- 4.4. Spoken-word recognition: Applying convnets on audio data -- Exercises -- Summary -- Chapter 5. Transfer learning: Reusing pretrained neural networks -- 5.1. Introduction to transfer learning: Reusing pretrained models -- 5.2. Object detection through transfer learning on a convnet -- Exercises -- Summary -- Part 3. Advanced deep learning with TensorFlow.js -- Chapter 6. Working with data -- 6.1. Using tf.data to manage data -- 6.2. Training models with model.fitDataset -- 6.3. Common patterns for accessing data -- 6.4. Your data is likely flawed: Dealing with problems in your data
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|a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324
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| 653 |
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|a JavaScript (Computer program language) / http://id.loc.gov/authorities/subjects/sh96004880
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| 653 |
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|a Artificial intelligence / fast
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| 653 |
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|a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a JavaScript (Langage de programmation)
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| 653 |
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|a Intelligence artificielle
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| 653 |
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|a JavaScript (Computer program language) / fast
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| 653 |
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|a Machine learning / fast
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| 653 |
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|a Apprentissage automatique
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| 653 |
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|a artificial intelligence / aat
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| 653 |
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|a Machine Learning
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| 653 |
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|a TensorFlow / http://id.loc.gov/authorities/names/n2019020612
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| 700 |
1 |
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|a Cai, Shanqing
|e author
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| 700 |
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|a Nielsen, Eric
|e author
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Made available through: Safari, an O'Reilly Media Company
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| 776 |
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|z 9781617296178
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781617296178/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3/1
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| 520 |
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|a In Deep Learning with JavaScript, you'll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you'll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation
|