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使用 TensorFlow 進行 JavaScript 開發作業
開始使用下方的學習教材之前，請先完成下列事項：
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熟悉使用 HTML、CSS 和 JavaScript 進行瀏覽器程式設計
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熟悉使用指令列執行 Node.js 指令碼
如果您想達成下列目標，就很適合參加本課程：
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使用 JavaScript 建構機器學習模型
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在可執行 JavaScript 的任何位置執行現有的模型
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將機器學習模型部署到網路瀏覽器
TensorFlow.js 能讓您使用 JavaScript 開發或執行機器學習模型，並直接在瀏覽器用戶端、透過 Node.js 在伺服器端、透過 React Native 在行動版原生應用程式中、透過 Electron 在電腦版原生應用程式中，甚至透過 Raspberry Pi 上的 Node.js 在物聯網裝置上使用機器學習技術。想進一步瞭解 TensorFlow.js 與其用途，請參考 Google I/O 大會的這場演講。
步驟 2：深入研究深度學習
Learning TensorFlow.js
作者：Gant Laborde
透過端對端實作的方式，協助各領域的技術人士掌握 TensorFlow.js 基礎知識。讀完這本書後，您將瞭解如何使用 TensorFlow.js 建構及部署可用於生產環境的深度學習系統。
Deep Learning with JavaScript
作者：Shanqing Cai、Stanley Bileschi、Eric D.Nielsen 和 Francois Chollet
本書是由 TensorFlow 程式庫的主要作者群所著，針對在瀏覽器或節點上以 JavaScript 進行深度學習的應用程式，提供有趣的使用案例和深入的操作說明。
步驟 3：使用 TensorFlow.js 練習範例
[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)"]]
