One of the most important processes that we can apply machine learning to is the preprocessing of feature vectors. Data in the real world is undoubtedly dirty and noisy in general. We need to pick the most useful features when it comes to prediction. Due to this, it is important to clean the data. This contributes not only to improving the accuracy of the prediction, but also reducing the time it takes for training by saving the size of the input data. In this chapter, we are going to introduce two popular ways to extract meaningful features from the original data. The first approach we will look at is principal component analysis (PCA), an unsupervised learning technique that projects the original data into a low-dimensional space. The other is an embedding technique that's typically used by NLP problems with t-SNE, which is a more advanced dimensionality...
Hands-On Machine Learning with TensorFlow.js
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Hands-On Machine Learning with TensorFlow.js
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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.
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Hands-On Machine Learning with TensorFlow.js
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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