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Hands-On Machine Learning with TensorFlow.js

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Hands-On Machine Learning with TensorFlow.js

Autor: Kai Sasaki
Broj strana: 296
ISBN broj: 9781838821739
Izdavač: PACKT PUBLISHING PACKT PUBLISHING
Godina izdanja: 2019.

Pregleda (30 dana / ukupno): 18 / 270

                 
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  • Use the t-SNE algorithm in TensorFlow.js to reduce dimensions in an input dataset
  • Deploy tfjs-converter to convert Keras models and load them into TensorFlow.js
  • Apply the Bellman equation to solve MDP problems
  • Use the k-means algorithm in TensorFlow.js to visualize prediction results
  • Create tf.js packages with Parcel, Webpack, and Rollup to deploy web apps
  • Implement tf.js backend frameworks to tune and accelerate app performance

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.

  • Build, train and run machine learning models in the browser using TensorFlow.js
  • Create smart web applications from scratch with the help of useful examples
  • Use flexible and intuitive APIs from TensorFlow.js to understand how machine learning algorithms function

Table of contents

1 Section 1: The Rationale of Machine Learning and the Usage of TensorFlow.js

2 Machine Learning for the Web
Technical requirements
Why machine learning on the web?
Operation graphs
What is TensorFlow.js?
Installing TensorFlow.js
The low-level API
The Layers API
Summary
Questions
Further reading

3 Importing Pretrained Models into TensorFlow.js
Technical requirements
The portable model format
Exporting a model from TensorFlow
Converting models using tfjs-converter
Loading the model into TensorFlow.js
Summary
Questions
Further reading

4 TensorFlow.js Ecosystem
Technical requirements
Why high-level libraries?
Using existing models
Loading the data from various kinds of storage
Pose detection with ML5.js
Drawing cats with Magenta.js
XOR classification with machinelearn.js
Summary
Exercises
Further reading

5 Section 2: Real-World Applications of TensorFlow.js

6 Polynomial Regression
Technical requirements
What is polynomial regression?
Two-dimensional curve fitting
Summary
Questions
Further reading

7 Classification with Logistic Regression
Technical requirements
Background of binary classification
What is logistic regression?
Classifying two-dimensional clusters
Summary
Questions
Further reading

8 Unsupervised Learning
Technical requirements
What is unsupervised learning?
Learning how K-means works
Generalizing K-means with the EM algorithm
Clustering two groups in a 2D space
Summary
Exercise
Further reading

9 Sequential Data Analysis
Technical requirements
What is Fourier transformation?
Cosine curve decomposition
Summary
Exercise
Further reading

10 Dimensionality Reduction
Technical requirements
Why dimensionality reduction?
Understanding principal component analysis
Projecting 3D points into a 2D space with PCA
Word embedding
Summary
Exercise
Further reading

11 Solving the Markov Decision Process
Technical requirements
Reinforcement learning
Solving the four-states environment
Summary
Exercise
Further reading

12 Section 3: Productionizing Machine Learning Applications with TensorFlow.js

13 Deploying Machine Learning Applications
Technical requirements
The ecosystem around the JavaScript platform
Module bundler
Deploying modules with GitHub Pages
Summary
Questions
Further reading

14 Tuning Applications to Achieve High Performance
Technical requirements
The backend API of TensorFlow.js
Tensor management
Asynchronous data access
Profiling
Model visualization
Summary
Questions
Further reading

15 Future Work Around TensorFlow.js
Technical requirements
Experimental backend implementations
AutoML edge helper
Summary
Questions
Further Reading

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