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Hands-On Transfer Learning with Python

You're reading from   Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

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Product type Paperback
Published in Aug 2018
Publisher Packt
ISBN-13 9781788831307
Length 438 pages
Edition 1st Edition
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Authors (4):
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Nitin Panwar Nitin Panwar
Author Profile Icon Nitin Panwar
Nitin Panwar
Raghav Bali Raghav Bali
Author Profile Icon Raghav Bali
Raghav Bali
Tamoghna Ghosh Tamoghna Ghosh
Author Profile Icon Tamoghna Ghosh
Tamoghna Ghosh
Dipanjan Sarkar Dipanjan Sarkar
Author Profile Icon Dipanjan Sarkar
Dipanjan Sarkar
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Table of Contents (14) Chapters Close

Preface 1. Machine Learning Fundamentals FREE CHAPTER 2. Deep Learning Essentials 3. Understanding Deep Learning Architectures 4. Transfer Learning Fundamentals 5. Unleashing the Power of Transfer Learning 6. Image Recognition and Classification 7. Text Document Categorization 8. Audio Event Identification and Classification 9. DeepDream 10. Style Transfer 11. Automated Image Caption Generator 12. Image Colorization 13. Other Books You May Enjoy

Setting up a robust, on-premise deep learning environment with GPU support

Often users or organizations may not want to leverage cloud services, especially if their data is sensitive, and so focus on building an on-premise deep learning environment. The major focus here should be to invest in the right type of hardware to enable maximum performance and leverage the right GPU for building deep learning models. With regards to hardware, special emphasis goes to the following:

  • Processor: You can invest in an i5 or an i7 Intel CPU, or maybe an Intel Xeon if you are looking to spoil yourself!
  • RAM: Invest in at least 32 GB of DDR4 or better RAM for your memory.
  • Disk: A 1 TB hard disk is excellent, and also you can invest in a minimum of 128 GB or 256 GB of SSD for fast data access!
  • GPU: Perhaps the most important component for deep learning. Invest in a NVIDIA GPU, anything above a...
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