- Find the PoseNet demo application in tfjs-models and run it in your local machine: https://github.com/tensorflow/tfjs-models/tree/master/posenet.
- Write an application that draws a circle where you click in the canvas area.
- Build an application that classifies the given image by using:
- MobileNet in tfjs-models
- Image Classifier in ML5.js (ml5.imageClassifier)
- Use the SketchRNN application to:
- Use the model for drawing a bird.
- By adjusting the temperature parameter of PDF, look into how the generated image is changed.
- Train RandomForestClassifier from machinelearn.js to predict the output value of OR, AND, and NAND logic.
- Build a pipeline, including preprocessing, as follows by using machinlearn.js:
- The input Iris dataset is available in machinelearn.js.
- The model is RandomForestClassifier.
- The model is evaluated by using train_test_split.
- The metric to evaluate...
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Book Overview & Buying
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Table Of Contents
Hands-On Machine Learning with TensorFlow.js
By :
Hands-On Machine Learning with TensorFlow.js
By:
Overview of this book
TensorFlow.js is a framework that enables you to create performant machine learning (ML) applications that run smoothly in a web browser. With this book, you will learn how to use TensorFlow.js to implement various ML models through an example-based approach.
Starting with the basics, you'll understand how ML models can be built on the web. Moving on, you will get to grips with the TensorFlow.js ecosystem to develop applications more efficiently. The book will then guide you through implementing ML techniques and algorithms such as regression, clustering, fast Fourier transform (FFT), and dimensionality reduction. You will later cover the Bellman equation to solve Markov decision process (MDP) problems and understand how it is related to reinforcement learning. Finally, you will explore techniques for deploying ML-based web applications and training models with TensorFlow Core. Throughout this ML book, you'll discover useful tips and tricks that will build on your knowledge.
By the end of this book, you will be equipped with the skills you need to create your own web-based ML applications and fine-tune models to achieve high performance.
Table of Contents (17 chapters)
Preface
Free Chapter
Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js
Machine Learning for the Web
Importing Pretrained Models into TensorFlow.js
TensorFlow.js Ecosystem
Section 2: Real-World Applications of TensorFlow.js
Polynomial Regression
Classification with Logistic Regression
Unsupervised Learning
Sequential Data Analysis
Dimensionality Reduction
Solving the Markov Decision Process
Section 3: Productionizing Machine Learning Applications with TensorFlow.js
Deploying Machine Learning Applications
Tuning Applications to Achieve High Performance
Future Work Around TensorFlow.js
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