Introduction to Deep Learning for Engineers
- Indbinding:
- Paperback
- Sideantal:
- 112
- Udgivet:
- 22. juli 2020
- Størrelse:
- 191x7x235 mm.
- Vægt:
- 226 g.
- Ukendt - mangler pt..
Normalpris
Abonnementspris
- Rabat på køb af fysiske bøger
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
- 1 valgfrit digitalt ugeblad
- 20 timers lytning og læsning
- Adgang til 70.000+ titler
- Ingen binding
Abonnementet koster 75 kr./md.
Ingen binding og kan opsiges når som helst.
Beskrivelse af Introduction to Deep Learning for Engineers
This book provides a short introduction and easy-to-follow implementation steps of deep learning using Google Cloud Platform. It also includes a practical case study that highlights the utilization of Python and related libraries for running a pre-trained deep learning model.
In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.
The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
In recent years, deep learning-based modeling approaches have been used in a wide variety of engineering domains, such as autonomous cars, intelligent robotics, computer vision, natural language processing, and bioinformatics. Also, numerous real-world engineering applications utilize an existing pre-trained deep learning model that has already been developed and optimized for a related task. However, incorporating a deep learning model in a research project is quite challenging, especially for someone who doesn't have related machine learning and cloud computing knowledge. Keeping that in mind, this book is intended to be a short introduction of deep learning basics through the example of a practical implementation case.
The audience of this short book is undergraduate engineering students who wish to explore deep learning models in their class project or senior design project without having a full journey through the machine learning theories. The case study part at the end also provides a cost-effective and step-by-step approach that can be replicated by others easily.
Brugerbedømmelser af Introduction to Deep Learning for Engineers
Giv din bedømmelse
For at bedømme denne bog, skal du være logget ind.Andre købte også..
Find lignende bøger
Bogen Introduction to Deep Learning for Engineers findes i følgende kategorier:
- Business og læring
- Teknologi, ingeniørvidenskab og landbrug > Teknologi: generelle emner > Ingeniørvidenskab: generelt
- Teknologi, ingeniørvidenskab og landbrug > Teknologi: generelle emner > Teknisk design
- Teknologi, ingeniørvidenskab og landbrug > Energiteknik > Elektroteknik
- Teknologi, ingeniørvidenskab og landbrug > Elektronik og kommunikationsteknik > Elektronik teknik
© 2024 Pling BØGER Registered company number: DK43351621