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 cloud-based deep learning environment with GPU support

Deep learning works quite well on a standard single PC setup with a CPU. However, once your datasets start increasing in size and your model architectures start getting more complex, you need to start thinking about investing in a robust deep learning environment. The major expectations being the system can build and train models efficiently, take less time to train models, and is fault tolerant. Most deep learning computations are essentially millions of matrix operations (data is represented as matrices) and enable fast computation in parallel; GPUs have been proven to work really well in this aspect. You can consider setting up a robust cloud-based deep learning environment or even an in-house environment. Let's look at how we can set up a robust cloud-based deep learning environment in this section.

The...