{"product_id":"practical-tensorflow-js-deep-learning-in-web-app-development","title":"Practical Tensorflow.Js: Deep Learning in Web App Development","description":"\u003cp\u003eChapter 1\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eWelcome to TensorFlow.js\u003c\/p\u003e\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● What is TensorFlow.js?\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● TensorFlow.js API\u003c\/p\u003e\n\u003cp\u003e○ Tensors\u003cbr\u003e○ Operations ○ Variables\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e● How to install it\u003c\/p\u003e\u003cp\u003e● Use cases\u003c\/p\u003e\u003cp\u003eChapter 2\u003c\/p\u003e\u003cp\u003eBuilding your First Model\u003c\/p\u003e\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Building a logistic regression classification model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Building a linear regression model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Doing unsupervised learning with k-means\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Dimensionality reduction and visualization with t-SNE and d3.js\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Our first neural network\u003c\/p\u003e\n\u003cp\u003eChapter 3\u003c\/p\u003e\n\u003cp\u003eCreate a drawing app to predict handwritten digits using\u003c\/p\u003e\n\u003cp\u003eConvolutional Neural Networks and MNIST\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Convolutional Neural Networks\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● The MNIST Dataset\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Design the model architecture\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Train the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Evaluate the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Build the drawing app\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Integrate the model within the app\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eChapter 4\u003c\/p\u003e\u003cp\u003e\"Move your body!\" A game featuring PoseNet, a pose estimator model\u003c\/p\u003e\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● What is PoseNet?\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Loading the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Interpreting the result\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Building a game around it\u003c\/p\u003e\n\u003cp\u003eChapter 5\u003c\/p\u003e\n\u003cp\u003eDetect yourself in real-time using an object detection model trained in\u003c\/p\u003e\n\u003cp\u003eGoogle Cloud's AutoML\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● TensorFlow Object Detection API\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Google Cloud's AutoML\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Training the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Exporting the model and importing it in TensorFlow.js\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Building the webcam app\u003c\/p\u003e\n\u003cp\u003eChapter 6\u003c\/p\u003e\n\u003cp\u003eTransfer Learning with Image Classifier and Voice Recognition\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● What's Transfer Learning?\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set)\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Transferring the knowledge\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Re-training the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Testing the model with a video\u003c\/p\u003e\n\u003cp\u003eChapter 7\u003c\/p\u003e\n\u003cp\u003eCensor food you do not like with pix2pix, Generative Adversarial\u003c\/p\u003e\n\u003cp\u003eNetworks, and ml5.js\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Introduction to Generative Adversarial Networks\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● What is image translation?\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Training your custom image translator with pix2pix\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Deploying the model with ml5.js\u003c\/p\u003e\n\u003cp\u003eChapter 8\u003c\/p\u003e\n\u003cp\u003eDetect toxic words from a Chrome Extension using a Universal\u003c\/p\u003e\n\u003cp\u003eSentence Encoder\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Toxicity classifier\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Training the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Testing the model\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Integrating the model in a Chrome Extension\u003c\/p\u003e\n\u003cp\u003eChapter 9\u003c\/p\u003e\n\u003cp\u003eTime Series Analysis and Text Generation with Recurrent Neural\u003c\/p\u003e\n\u003cp\u003eNetworks\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Recurrent Neural Networks\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Example 1: Building an RNN for time series analysis\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\n\u003cp\u003e● Example 2: Building an RNN to generate text\u003c\/p\u003e\n\u003cp\u003eChapter 10\u003c\/p\u003e\n\u003cp\u003eBest practices, integrations with other platforms, remarks and final\u003c\/p\u003e\n\u003cp\u003ewords\u003c\/p\u003e\n\u003cp\u003eHeadings\u003cbr\u003e\u003c\/p\u003e\n\u003c\/li\u003e\n\u003c\/ul\u003e\u003cul\u003e\n\u003cli\u003e\u003cp\u003e● Best practices\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Integration with other platforms\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Materials for further practice\u003c\/p\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cp\u003e● Conclusion\u003c\/p\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=AUTH-13748710\"\u003eJuan de Dios Santos Rivera\u003c\/a\u003e\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Apress\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 09\/19\/2020\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 303\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.01lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.21h x 6.14w x 0.69d\u003cbr\u003e\u003cb\u003eISBN13:\u003c\/b\u003e 9781484262726\u003cbr\u003e\u003cb\u003eISBN10:\u003c\/b\u003e 1484262727\u003cbr\u003e\u003cb\u003eBISAC Categories:\u003c\/b\u003e\u003cbr\u003e- \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=CAT-COM\"\u003eComputers\u003c\/a\u003e | \u003ca href=\"https:\/\/correctionsbookstore.com\/search?type=product%2Carticle%2Cpage\u0026amp;q=BISAC-COM004000\"\u003eArtificial Intelligence | General\u003c\/a\u003e\u003cbr\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eJuan De Dios Santos Rivera\u003c\/b\u003e is a machine learning engineer who focuses on building data-driven and machine learning-driven platforms. As a Big Data Software Engineer for mobile apps, his role has been to build solutions to detect spammers and avoid the proliferation of them. This book goes hand-to-hand with that role in building data solutions. As the AI field keeps growing, developers need to keep extending the reach of our products to every platform out there, which includes web browsers.\u003c\/p\u003e","brand":"Apress","offers":[{"title":"Default Title","offer_id":45876730790063,"sku":"9781484262726","price":69.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0651\/9255\/8767\/files\/img_5280852f-523b-4bab-b163-c4c48cab812e.jpg?v=1757694698","url":"https:\/\/www.correctionsbookstore.com\/es\/products\/practical-tensorflow-js-deep-learning-in-web-app-development","provider":"Corrections Bookstore ","version":"1.0","type":"link"}