

Learning Tensorflow.Js: Powerful Machine Learning in JavaScript (Paperback)
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- Book formatPaperback
- Fiction/nonfictionNon-Fiction
- GenreComputing & Internet
- Pages338
- Reading levelProfessional and Scholarly
- SubgenreComputers
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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.
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-ready deep learning systems with TensorFlow.js.
- Explore tensors, the most fundamental structure of machine learning
- Convert data into tensors and back with a real-world example
- Combine AI with the web using TensorFlow.js
- Use resources to convert, train, and manage machine learning data
- Build and train your own training models from scratch
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| Fiction/nonfiction | Non-FictionLearning Tensorflow.Js: Powerful Machine Learning in JavaScript (Paperback) | Non-FictionMachine Learning: The 10 Classifiers in Python, (Paperback) | Non-FictionMastering Computer Vision with PyTorch and Machine Learning, (Paperback) | Non-FictionTensorFlow: Powerful Predictive Analytics with TensorFlow, (Paperback) | Non-FictionBeginning Machine Learning in the Browser: Quick-Start Guide to Gait Analysis with JavaScript and Tensorflow.Js, (Paperback) | Non-FictionGoogle JAX Cookbook: Perform machine learning and numerical computing with combined capabilities of TensorFlow and NumPy, (Paperback) | Non-FictionLearning PyTorch 2.0, Second Edition: Utilize PyTorch 2.3 and CUDA 12 to experiment neural networks and deep learning mo, (Paperback) | Non-FictionMachine Learning with scikit-learn Quick Start Guide (Paperback) | Non-FictionPractical Machine Learning in JavaScript: Tensorflow.Js for Web Developers, (Paperback) | Non-FictionMachine Learning Algorithms: Handbook (Paperback) |
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