Develop and deploy deep learning web apps using the TensorFlow.js library. TensorFlow.js is part of a bigger framework named TensorFlow, which has many tools that supplement it, such as TensorBoard, ml5js, tfjs-vis. This book will cover all these technologies and show they integrate with TensorFlow.js to create intelligent web apps. The most common and accessible platform users interact with everyday is their web browser, making it an ideal environment to deploy AI systems. TensorFlow.js is a well-known and battle-tested library for creating browser solutions. Working in JavaScript, the so-called language of the web, directly on a browser, you can develop and serve deep learning applications.
You'll work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN). Through hands-on examples, apply these networks in use cases related to image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis.
Also, these topics are very varied in terms of the kind of data they use, their output, and the training phase. Not everything in machine learning is deep networks, there is also what some call shallow or traditional machine learning. While TensorFlow.js is not the most common place to implement these, you'll be introduce them and review the basics of machine learning through TensorFlow.js. What You'll Learn
Build deep learning products suitable for web browsers
Work with deep learning algorithms such as feedforward neural networks, convolutional neural networks (CNN), recurrent neural networks (RNN), and generative adversarial network (GAN)
Develop apps using image classification, natural language processing, object detection, dimensionality reduction, image translation, transfer learning, and time series analysis
Who This Book Is For
Programmers developing deep learning solutions for the web and those who want to learn TensorFlow.js with at least minimal programming and software development knowledge. No prior JavaScript knowledge is required, but familiarity with it is helpful.
By:
Juan De Dios Santos Rivera
Imprint: APress
Country of Publication: United States
Edition: 1st ed.
Dimensions:
Height: 235mm,
Width: 155mm,
Weight: 504g
ISBN: 9781484262726
ISBN 10: 1484262727
Pages: 303
Publication Date: 19 September 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
"Chapter 1 Welcome to TensorFlow.js Headings ● What is TensorFlow.js? ● TensorFlow.js API ○ Tensors ○ Operations ○ Variables ● How to install it ● Use cases Chapter 2 Building your First Model Headings ● 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 model Headings ● 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"
Juan 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.
