One of the primary reasons for the widespread popularity and adoption of deep learning is the Python deep learning ecosystem, which consists of easy-to-use open source deep learning frameworks. However, the deep learning landscape is rapidly changing, considering how new frameworks keep getting launched and older ones reach the end of their life. Deep learning enthusiasts might know that Theano was the first and most popular deep learning framework, created by MILA (https://mila.quebec/), which was headed by Yoshua Bengio. Unfortunately, it was recently announced that further development and support for Theano is ending after the launch of its latest version (1.0) in 2017. Hence, it is of paramount importance to understand what frameworks are out there that can be leveraged to implement and solve deep learning. Another point to remember here is that several...
Hands-On Transfer Learning with Python
By :
Hands-On Transfer Learning with Python
By:
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.
Related Content you might be interested in
Current Title:
Hands-On Transfer Learning with Python
Table of Contents (14 chapters)
Preface
Free Chapter
Machine Learning Fundamentals
Deep Learning Essentials
Understanding Deep Learning Architectures
Transfer Learning Fundamentals
Unleashing the Power of Transfer Learning
Image Recognition and Classification
Text Document Categorization
Audio Event Identification and Classification
DeepDream
Style Transfer
Automated Image Caption Generator
Image Colorization
Other Books You May Enjoy
Customer Reviews