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    Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease

    (ebook) (audiobook) (audiobook) Książka w języku 1
    Autorzy:
    Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán
    Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán - okladka książki

    Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán - okladka książki

    Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán - audiobook MP3

    Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease Bo Wang, Cristian Mitroi, Feng Wang, Shubham Saboo, Susana Guzmán - audiobook CD

    Wydawnictwo:
    Packt Publishing (Z chęcią przeczytam książkę w języku polskim)
    Ocena:
    Bądź pierwszym, który oceni tę książkę
    Stron:
    188
    Dostępne formaty:
         PDF
         ePub
    Search is a big and ever-growing part of the tech ecosystem. 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 time and complexity involved in creating deep learning–powered search engines. 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 engine. 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 engine.
    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.

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    O autorach książki

    Bo Wang is a machine learning engineer at Jina AI. He has a background in computer science, especially interested in the field of information retrieval. In the past years, he has been conducting research and engineering work on search intent classification, search result diversification, content-based image retrieval, and neural information retrieval. At Jina AI, Bo is working on developing a platform for automatically improving search quality with deep learning. In his spare time, he likes to play with his cats, watch anime, and play mobile games.
    Cristian Mitroi is a machine learning engineer with a wide breadth of experience in full stack, from infrastructure to model iteration and deployment. His background is based in linguistics, which led to him focusing on NLP. He also enjoys, and has experience in, teaching and interacting with the community, and has given workshops at various events. In his spare time, he performs improv comedy and organizes too many pen-and-paper role-playing games.
    Feng Wang is a machine learning engineer at Jina AI. He received his Ph.D. from the department of computer science at the Hong Kong Baptist University in 2018. He has been a full-time R&D engineer for the past few years, and his interests include data mining and artificial intelligence, with a particular focus on natural language processing, multi-modal representation learning, and recommender systems. In his spare time, he likes climbing, hiking, and playing mobile games.
    Shubham Saboo has played multiple roles from a data scientist to an AI evangelist at renowned firms across the globe, where he was involved in building organization-wide data strategies and technology infrastructure to create and scale data science and machine learning practice from scratch. His work as an AI evangelist has led him to build communities and reach out to a broader audience to foster the exchange of ideas and thoughts in the burgeoning field of artificial intelligence. As part of his passion for learning new things and sharing knowledge with the community, he writes technical blogs on the advancements in AI and its economic implications. In his spare time, you can find him travelling across the country which enables him to immerse in different cultures and refine his worldview based on his experiences.

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