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使用 JavaScript 进行 TensorFlow 开发
在开始学习下面的学习资料之前，您应该：
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对使用 HTML、CSS 和 JavaScript 进行浏览器编程有所了解
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熟悉如何使用命令行运行 Node.js 脚本
本课程适用于想要实现以下目标的人员：
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用 JavaScript 构建机器学习模型
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在能运行 JavaScript 的任何环境中运行现有模型
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将机器学习模型部署到网络浏览器中
借助 TensorFlow.js，您可以使用 JavaScript 开发或执行机器学习模型，并直接在浏览器客户端中使用机器学习技术，或者通过 Node.js、React Native、Electron，甚至是 Raspberry Pi 上的 Node.js，在服务器端、移动原生设备、桌面原生设备以及 IoT 设备中使用机器学习技术。如需详细了解 TensorFlow.js 及其作用，请观看 Google I/O 大会上的这段演讲视频。
第 2 步：更加深入地了解深度学习
了解 TensorFlow.js
由 Gant Laborde 编著
通过端到端的实际操作方法学习 TensorFlow.js 基础知识，适合广泛的技术受众。读完本书后，您将了解如何使用 TensorFlow.js 构建和部署可用于生产环境的深度学习系统。
使用 JavaScript 进行深度学习
由 Shanqing Cai、Stanley Bileschi、Eric D. Nielsen 和 Francois Chollet 共同编著
本书由 TensorFlow 库的主要作者编著，提供了关于在浏览器中或 Node 上使用 JavaScript 构建深度学习应用的精彩用例和深入说明。
第 3 步：使用 TensorFlow.js 进行示例练习
[null,null,[],[],[],null,["# Basics of TensorFlow for JavaScript development\n\n[TensorFlow](/tutorials) › [Resources](/resources/models-datasets) › [Learn ML](/resources/learn-ml) › [Guide](/resources/learn-ml/basics-of-tensorflow-for-js-development) › \n\nTensorFlow for JavaScript development\n=====================================\n\nBefore starting on the learning materials below, you should:\n\n1. Be comfortable with browser programming using HTML, CSS, \\& JavaScript\n\n2. Be familiar with using the command line to run Node.js scripts\n\nThis curriculum is for people who want to:\n\n1. Build ML models in JavaScript\n\n2. Run existing models anywhere Javascript can run\n\n3. Deploy ML models to web browsers\n\nTensorFlow.js lets you develop or execute ML models in JavaScript, and use ML directly in the browser client side, server side via Node.js, mobile native via React Native, desktop native via Electron, and even on IoT devices via Node.js on Raspberry Pi. To learn more about TensorFlow.js, and what can be done with it, check out [this talk](https://www.youtube.com/watch?v=uU-u-5Eo65g) at Google I/O. \n\nStep 1: Get introduced to machine learning in the browser\n---------------------------------------------------------\n\nTo get a quick introduction on basics for ML in JavaScript, take the self-paced [course on Edx](https://www.edx.org/course/google-ai-for-javascript-developers-with-tensorflowjs) or watch the videos below that take you from first principles, to using existing pre-made models, and even building your own neural network for classification. You can also try the [Make a smart webcam in JavaScript](https://codelabs.developers.google.com/codelabs/tensorflowjs-object-detection#0) Codelab for an interactive walkthrough of these concepts. \n\nSuperpowers for next gen web apps: Machine Learning \nThis high level intro to machine learning in JavaScript is for web developers looking to take their first steps with TensorFlow.js. \nFree\nWatch video\n\nCode \nTheory \n*close* \n[Google AI for JavaScript developers with TensorFlow.js](https://www.edx.org/course/google-ai-for-javascript-developers-with-tensorflowjs) \nGo from zero to hero with web ML using TensorFlow.js. Learn how to create next generation web apps that can run client side and be used on almost any device. \nFree [View course](https://www.edx.org/course/google-ai-for-javascript-developers-with-tensorflowjs) \nBuild \nCode \nTheory \nBuild \n[Make a smart webcam in JavaScript with a pre-trained model](https://codelabs.developers.google.com/codelabs/tensorflowjs-object-detection#0) \nLearn how to load and use one of the TensorFlow.js pre-trained models (COCO-SSD) and use it to recognize common objects it's been trained on. \nFree [See Codelab](https://codelabs.developers.google.com/codelabs/tensorflowjs-object-detection#0) \nTheory \nBuild \n\nStep 2: Dive deeper into Deep Learning\n--------------------------------------\n\nTo get a deeper understanding of how neural networks work, and a broader understanding of how to apply them to different problems, we have two books available.\n\n[Learning TensorFlow.js](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) is a great place to start if you are new to Tensors and Machine Learning generally but have a good understanding of JavaScript. This book takes you all the way from the basics such as understanding how to manipulate data into Tensors, to quickly progressing to real world applications. After reading, you will understand how to load existing models, pass data to them, and interpret data that comes out.\n\n[Deep Learning with JavaScript](https://www.manning.com/books/deep-learning-with-javascript) is also a great place to start. It is accompanied by a large number of examples from GitHub so you can practice working with machine learning in JavaScript.\n\nThis book will demonstrate how to use a wide variety of neural network architectures, such as Convolutional Neural Networks, Recurrent Neural Networks, and advanced training paradigms such as reinforcement learning. It also provides clear explanations of what is actually happening with the neural network in the training process. \n[Learning TensorFlow.js](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) \nby Gant Laborde \nA hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience. Once you finish this book, you'll know how to build and deploy production-ready deep learning systems with TensorFlow.js. \n[View book](https://www.oreilly.com/library/view/learning-tensorflowjs/9781492090786/) \nCode \nTheory \nBuild \n[Deep Learning with JavaScript](https://www.manning.com/books/deep-learning-with-javascript) \nby Shanqing Cai, Stanley Bileschi, Eric D. Nielsen with Francois Chollet \nWritten by the main authors of the TensorFlow library, this book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node. \n[View book](https://www.manning.com/books/deep-learning-with-javascript) \nCode \nTheory \nBuild \n\nStep 3: Practice with examples using TensorFlow.js\n--------------------------------------------------\n\nPractice makes perfect, and getting hands on experience is the best way to lock in the concepts. Check out the [TensorFlow.js](https://codelabs.developers.google.com/s/results?q=tensorflow.js) codelabs to further your knowledge with these step by step guides for common use cases:\n\n1. [Make your very own \"Teachable Machine\" from a blank canvas](https://codelabs.developers.google.com/codelabs/tensorflowjs-teachablemachine-codelab#0)\n\n2. [Handwritten digit recognition with Convolutional Neural Networks](https://codelabs.developers.google.com/codelabs/tfjs-training-classfication#0)\n\n3. [Make predictions from 2D data](https://codelabs.developers.google.com/codelabs/tfjs-training-regression#0)\n\n4. [Convert a Python SavedModel to TensorFlow.js format](https://codelabs.developers.google.com/codelabs/tensorflowjs-convert-python-savedmodel#0)\n\n5. [Use Firebase to deploy and host a TensorFlow.js model](https://codelabs.developers.google.com/codelabs/tensorflowjs-firebase-hosting-model#0)\n\n6. [Build a comment spam detection system](https://codelabs.developers.google.com/codelabs/tensorflowjs-comment-spam-detection#0)\n\n7. [Retrain a comment spam detection model to handle custom edge cases](https://codelabs.developers.google.com/tensorflow-retraining-comment-spam-detection#0)\n\n8. [Audio recognition using transfer learning](https://codelabs.developers.google.com/codelabs/tensorflowjs-audio-codelab#0)\n\nWith your knowledge of neural networks, you can more easily explore the [open sourced examples](https://github.com/tensorflow/tfjs-examples) created by the TensorFlow team. They are all [available on GitHub](https://github.com/tensorflow/tfjs-examples), so you can delve into the code and see how they work. \n[Examples built with TensorFlow.js](https://github.com/tensorflow/tfjs-examples) \nA repository on GitHub that contains a set of examples implemented in TensorFlow.js. Each example directory is standalone so the directory can be copied to another project. \nFree [Learn more](https://github.com/tensorflow/tfjs-examples) \nCode \nBuild \n[Explore our tutorials to learn how to get started with TensorFlow.js](/js/tutorials) \nThe TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab---a hosted notebook environment that requires no setup. Click the Run in Google Colab button. \nFree [Learn more](/js/tutorials) \nCode \nBuild\n\nStep 4: Make something new!\n---------------------------\n\nOnce you've tested your knowledge, and practiced with some of the TensorFlow.js examples, you should be ready to start developing your own projects. Take a look at our [pretrained models](/js/models), and start building an app in minutes. Or you can train your own model using data you've collected, or by using public datasets. [Kaggle](https://www.kaggle.com/datasets) and [Google Dataset Search](https://toolbox.google.com/datasetsearch) are great places to find open datasets for training your model.\n\nIf you are looking for inspiration, check out our [Made With TensorFlow.js show and tell episodes](https://www.youtube.com/playlist?list=PLQY2H8rRoyvzSZZuF0qJpoJxZR1NgzcZw) from people all around the world who have used TensorFlow.js in their applications.\n\nYou can also see the latest contributions from the community by searching for the [#MadeWithTFJS](https://twitter.com/hashtag/MadeWithTFJS?src=hashtag_click&f=live) hashtag on social media. \n[Previous\nTheoretical and advanced machine learning with TensorFlow](/resources/learn-ml/theoretical-and-advanced-machine-learning) \n\nLearn, develop and build with TensorFlow\n----------------------------------------\n\n[Get started](/learn)"]]
