Chapter 1, Neural Networks for Neural Search, will cover what neural search is and what it is used for. You’ll see an overview of its practical applications, the challenges it poses, and how to overcome them.

Chapter 2, Introducing Foundations of Vector Representation, will cover the concept of vectors in ML. It will also introduce you to common search algorithms built on top of vector representations and their strengths and weaknesses.

Chapter 3, System Design and Engineering Challenges, will cover the basics of designing a search system. In this chapter, you will learn the core concepts associated with search, such as indexing and querying, and how to use this to save and retrieve information.

Chapter 4, Learning Jina’s Basics, will cover the steps required to implement your own search engine. In this chapter, you will learn the core concepts of Jina in detail, along with their overall design and how they connect with each other.

Chapter 5, Multiple Search Modalities, will introduce you to the concept of multi-modal and cross-modal search, where you can combine multiple modalities, such as text, image, audio, and video, to build state-of-the-art search systems.

Chapter 6, Basic Practical Examples with Jina, will cover beginner-friendly real-world applications of Jina’s neural search framework by building on the concepts learned in the previous chapters.

Chapter 7, Exploring Advanced Use Cases of Jina, will cover the advanced applications of Jina’s neural search framework by using the concepts learned in the previous chapters. It will focus on explaining challenging concepts in neural search using real-world examples.