Deep Learning on Microcontrollers (notice n° 76929)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03444cam a2200289zu 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | FRCYB88942566 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20250108001551.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 250108s2023 fr | o|||||0|0|||eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789355518057 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | FRCYB88942566 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | FR-PaCSA |
| Language of cataloging | en |
| Transcribing agency | |
| Description conventions | rda |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Krishna Gupta, Atul |
| 245 01 - TITLE STATEMENT | |
| Title | Deep Learning on Microcontrollers |
| Remainder of title | Learn how to develop embedded AI applications using TinyML |
| Statement of responsibility, etc. | ['Krishna Gupta, Atul', 'Prasad Nandyala, Siva'] |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Name of producer, publisher, distributor, manufacturer | BPB Publications |
| Date of production, publication, distribution, manufacture, or copyright notice | 2023 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | p. |
| 336 ## - CONTENT TYPE | |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type code | c |
| Source | rdamdedia |
| 338 ## - CARRIER TYPE | |
| Carrier type code | c |
| Source | rdacarrier |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc. | A step-by-step guide that will teach you how to deploy TinyML on microcontrollers Key Features ? Deploy machine learning models on edge devices with ease. ? Leverage pre-built AI models and deploy them without writing any code. ? Create smart and efficient IoT solutions with TinyML. Description TinyML, or Tiny Machine Learning, is used to enable machine learning on resource-constrained devices, such as microcontrollers and embedded systems. If you want to leverage these low-cost, low-power but strangely powerful devices, then this book is for you. This book aims to increase accessibility to TinyML applications, particularly for professionals who lack the resources or expertise to develop and deploy them on microcontroller-based boards. The book starts by giving a brief introduction to Artificial Intelligence, including classical methods for solving complex problems. It also familiarizes you with the different ML model development and deployment tools, libraries, and frameworks suitable for embedded devices and microcontrollers. The book will then help you build an Air gesture digit recognition system using the Arduino Nano RP2040 board and an AI project for recognizing keywords using the Syntiant TinyML board. Lastly, the book summarizes the concepts covered and provides a brief introduction to topics such as zero-shot learning, one-shot learning, federated learning, and MLOps. By the end of the book, you will be able to develop and deploy end-to-end Tiny ML solutions with ease. What you will learn ? Learn how to build a Keyword recognition system using the Syntiant TinyML board. ? Learn how to build an air gesture digit recognition system using the Arduino Nano RP2040. ? Learn how to test and deploy models on Edge Impulse and Arduino IDE. ? Get tips to enhance system-level performance. ? Explore different real-world use cases of TinyML across various industries. Who this book is for The book is for IoT developers, System engineers, Software engineers, Hardware engineers, and professionals who are interested in integrating AI into their work. This book is a valuable resource for Engineering undergraduates who are interested in learning about microcontrollers and IoT devices but may not know where to begin. Table of Contents 1. Introduction to AI 2. Traditional ML Lifecycle 3. TinyML Hardware and Software Platforms 4. End-to-End TinyML Deployment Phases 5. Real World Use Cases 6. Practical Experiments with TinyML 7. Advance Implementation with TinyML Board 8. Continuous Improvement 9. Conclusion |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Krishna Gupta, Atul |
| 700 0# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Prasad Nandyala, Siva |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Access method | Cyberlibris |
| Uniform Resource Identifier | <a href="https://international.scholarvox.com/netsen/book/88942566">https://international.scholarvox.com/netsen/book/88942566</a> |
| Electronic format type | text/html |
| Host name | |
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