Traditional search, however, has limitations that are hard to overcome because of the way it is designed. Neural search is a novel approach that uses the power of machine learning to retrieve information using vector embeddings as first-class citizens, opening up new possibilities of improving the results obtained through traditional search.

Although neural search is a powerful tool, it is new and finetuning it can be tedious as it requires you to understand the several components on which it relies. Jina fills this gap by providing an infrastructure that reduces the and complexity involved in creating deep learning–powered search ees. This book will enable you to learn the fundamentals of neural networks for neural search, its strengths and weaknesses, as well as how to use Jina to build a search ee. With the help of step-by-step explanations, practical examples, and self-assessment questions, you'll become well-versed with the basics of neural search and core Jina concepts, and learn to apply this knowledge to build your own search ee.

By the end of this deep learning book, you'll be able to make the most of Jina's neural search design patterns to build an end-to-end search solution for any modality.

What you will learn
Understand how neural search and legacy search work
Grasp the machine learning and math fundamentals needed for neural search
Get to grips with the foundation of vector representation
Explore the basic components of Jina
Analyze search systems with different modalities
Uncover the capabilities of Jina with the help of practical examples
Who this book is for
If you are a machine learning, deep learning, or artificial intelligence eeer interested in building a search system of any kind (text, QA, image, audio, PDF, 3D models, or others) using modern software architecture, this book is for you. This book is perfect for Python eeers who are interested in building a search system of any kind using state-of-the-art deep learning techniques.



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