Generative AI System Design Interview 296047
Код товару: 296047Паперова книга
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ISBN9781736049143
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Бренд
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Автор
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Рік2024
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МоваАнглійська
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ІлюстраціїЧорно-білі, Схеми
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ЖанрДовідкова література, Глибоке навчання, Штучний інтелект
Designing generative AI (GenAI) systems for interviews can be complex and challenging. This book offers a clear and structured approach to help you tackle a wide range of GenAI system design questions. It provides a practical framework and real-world examples to make learning these concepts easier.
This book complements our ML System Design Interview book. While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.
What’s inside?
This book complements our ML System Design Interview book. While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.
What’s inside?
- An insider’s perspective on what interviewers are truly looking for and why.
- A 7-step framework to help you tackle GenAI system design interview questions.
- 10 real-world GenAI system design questions with in-depth solutions.
- 280+ diagrams to demystify complex GenAI systems.
About the authors
Hao Sheng is a researcher and engineer specializing in AI and machine learning with over 10 years of expertise. He holds a Ph.D. in Computer Engineering from Stanford University and has worked at OpenAI, Apple, TikTok, and Landing AI. His expertise spans recommendation systems, computer vision, and generative AI. Hao has also taught "Search and Recommendation Systems" at Stanford.
Ali Aminian is an author and a Staff ML engineer with +10 years of expertise working in tech companies (Adobe, Ex-Google) building large-scale and distributed ML systems.
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Характеристики
- Бренд
- Вага, г615
- Автор
- КатегоріяКомп'ютерна література
- Рік2024
- Сторінок378
- Формат180х255 мм
- ОбкладинкаМ'яка
- Тип паперуОфсетний
- МоваАнглійська
- ІлюстраціїЧорно-біліСхеми
- ЖанрДовідкова літератураГлибоке навчанняШтучний інтелект
Від видавця
Designing generative AI (GenAI) systems for interviews can be complex and challenging. This book offers a clear and structured approach to help you tackle a wide range of GenAI system design questions. It provides a practical framework and real-world examples to make learning these concepts easier.
This book complements our ML System Design Interview book. While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.
What’s inside?
This book complements our ML System Design Interview book. While the first book focuses on topics such as search and recommendation systems, this one centers on generative systems, with detailed examples and explanations to help you understand how GenAI systems are built in practice.
What’s inside?
- An insider’s perspective on what interviewers are truly looking for and why.
- A 7-step framework to help you tackle GenAI system design interview questions.
- 10 real-world GenAI system design questions with in-depth solutions.
- 280+ diagrams to demystify complex GenAI systems.
About the authors
Hao Sheng is a researcher and engineer specializing in AI and machine learning with over 10 years of expertise. He holds a Ph.D. in Computer Engineering from Stanford University and has worked at OpenAI, Apple, TikTok, and Landing AI. His expertise spans recommendation systems, computer vision, and generative AI. Hao has also taught "Search and Recommendation Systems" at Stanford.
Ali Aminian is an author and a Staff ML engineer with +10 years of expertise working in tech companies (Adobe, Ex-Google) building large-scale and distributed ML systems.
Зміст
Chapter 1 Introduction and Overview
Chapter 2 Gmail Smart Compose
Chapter 3 Google Translate
Chapter 4 ChatGPT: Personal Assistant Chatbot
Chapter 5 Image Captioning
Chapter 6 Retrieval-Augmented Generation
Chapter 7 Realistic Face Generation
Chapter 8 High-Resolution Image Synthesis
Chapter 9 Text-to-Image Generation
Chapter 10 Personalized Headshot Generation
Chapter 11 Text-to-Video Generation
Chapter 2 Gmail Smart Compose
Chapter 3 Google Translate
Chapter 4 ChatGPT: Personal Assistant Chatbot
Chapter 5 Image Captioning
Chapter 6 Retrieval-Augmented Generation
Chapter 7 Realistic Face Generation
Chapter 8 High-Resolution Image Synthesis
Chapter 9 Text-to-Image Generation
Chapter 10 Personalized Headshot Generation
Chapter 11 Text-to-Video Generation
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