Book Image

Hands-On Transfer Learning with Python

By : Nitin Panwar, Sarkar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Nitin Panwar, Sarkar, Raghav Bali, Tamoghna Ghosh

Overview of this book

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems.
Table of Contents (14 chapters)

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...