Chapter 1Welcome to TensorFlow.jsHeadings ● What is TensorFlow.js? ● TensorFlow.js API ○ Tensors○ Operations ○ Variables ● How to install it● Use casesChapter 2Building your First ModelHeadings ● Building a logistic regression classification model ● Building a linear regression model ● Doing unsupervised learning with k-means ● Dimensionality reduction and visualization with t-SNE and d3.js ● Our first neural network Chapter 3 Create a drawing app to predict handwritten digits using Convolutional Neural Networks and MNIST Headings ● Convolutional Neural Networks ● The MNIST Dataset ● Design the model architecture ● Train the model ● Evaluate the model ● Build the drawing app ● Integrate the model within the app Chapter 4"Move your body!" A game featuring PoseNet, a pose estimator modelHeadings ● What is PoseNet? ● Loading the model ● Interpreting the result ● Building a game around it Chapter 5 Detect yourself in real-time using an object detection model trained in Google Cloud's AutoML Headings ● TensorFlow Object Detection API ● Google Cloud's AutoML ● Training the model ● Exporting the model and importing it in TensorFlow.js ● Building the webcam app Chapter 6 Transfer Learning with Image Classifier and Voice Recognition Headings ● What's Transfer Learning? ● MobileNet and ImageNet (MobileNet is the base model and ImageNet is the training set) ● Transferring the knowledge ● Re-training the model ● Testing the model with a video Chapter 7 Censor food you do not like with pix2pix, Generative Adversarial Networks, and ml5.js Headings ● Introduction to Generative Adversarial Networks ● What is image translation? ● Training your custom image translator with pix2pix ● Deploying the model with ml5.js Chapter 8 Detect toxic words from a Chrome Extension using a Universal Sentence Encoder Headings ● Toxicity classifier ● Training the model ● Testing the model ● Integrating the model in a Chrome Extension Chapter 9 Time Series Analysis and Text Generation with Recurrent Neural Networks Headings ● Recurrent Neural Networks ● Example 1: Building an RNN for time series analysis ● Example 2: Building an RNN to generate text Chapter 10 Best practices, integrations with other platforms, remarks and final words Headings ● Best practices ● Integration with other platforms ● Materials for further practice ● Conclusion Author: Juan de Dios Santos RiveraPublisher: ApressPublished: 09/19/2020Pages: 303Binding Type: PaperbackWeight: 1.01lbsSize: 9.21h x 6.14w x 0.69dISBN13: 9781484262726ISBN10: 1484262727BISAC Categories:- Computers | Artificial Intelligence | GeneralAbout the AuthorJuan De Dios Santos Rivera 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.
