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Python Machine Learning By Example

Python Machine Learning By Example - Fourth Edition

By : Yuxi (Hayden) Liu
4.9 (8)
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Python Machine Learning By Example

Python Machine Learning By Example

4.9 (8)
By: Yuxi (Hayden) Liu

Overview of this book

The fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.
Table of Contents (18 chapters)
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16
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Index

Building an Image Search Engine Using CLIP: a Multimodal Approach

In the previous chapter, we focused on Transformer models such as BERT and GPT, leveraging their capabilities for sequence learning tasks. In this chapter, we’ll explore a multimodal model, which seamlessly connects visual and textual data. With its dual encoder architecture, this model learns the relationships between visual and textual concepts, enabling it to excel in tasks involving image and text. We will delve into its architecture, key components, and learning mechanisms, leading to a practical implementation of the model. We will then build a multimodal image search engine with text-to-image and image-to-image capabilities. To top it all off, we will tackle an awesome zero-shot image classification project!

We will cover the following topics in this chapter:

  • Introducing the CLIP model
  • Getting started with the dataset
  • Architecting the CLIP model
  • Finding images with words...
Visually different images
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Python Machine Learning By Example
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