Deep Learning with JavaScript
Neural networks in TensorFlow.js
Seiten
2020
Manning Publications (Verlag)
978-1-61729-617-8 (ISBN)
Manning Publications (Verlag)
978-1-61729-617-8 (ISBN)
Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R.
Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.
TensorFlow.js is an open-source JavaScript library for defining, training, and deploying deep learning models to the web browser. It's quickly gaining popularity with developers for its amazing set of benefits including scalability, responsiveness, modularity, and portability.
Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.
- Deploying computer vision, audio, and natural language processing in the browser
- Fine-tuning machine learning models with client-side data
- Constructing and training a neural network
- Interactive AI for browser games using deep reinforcement learning
- Generative neural networks to generate music and pictures
TensorFlow.js is an open-source JavaScript library for defining, training, and deploying deep learning models to the web browser. It's quickly gaining popularity with developers for its amazing set of benefits including scalability, responsiveness, modularity, and portability.
Shanging Cai and Eric Nielsen are senior software engineers on the Google Brain team.
Stan Bileschi is the technical lead for Google's TensorFlow Usability team, which built the TensorFlow Layers API. All three have advanced degrees from MIT. Together, they're responsible for writing most of TensorFlow.js.
| Erscheinungsdatum | 07.03.2019 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Maße | 190 x 240 mm |
| Gewicht | 946 g |
| Einbandart | kartoniert |
| Themenwelt | Mathematik / Informatik ► Informatik ► Programmiersprachen / -werkzeuge |
| Informatik ► Web / Internet ► JavaScript | |
| Schlagworte | Deep learning • JavaScript • TensorFlow.js |
| ISBN-10 | 1-61729-617-1 / 1617296171 |
| ISBN-13 | 978-1-61729-617-8 / 9781617296178 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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