The word architecture refers to the overall structure of the neural network, including how many layers it can have and how units in the layers should be connected to each other (for instance, units across successive layers can be fully connected, partially connected, or may even skip the next layer altogether and then make connections to a layer at a much higher level in the network). With the availability of modular deep learning frameworks, such as Caffe, Torch, and TensorFlow, complex neural network designs have been revolutionized. Now we can compare neural network designs to Lego blocks, where you can build almost any structure that you can imagine. However, these designs are not just random guesses. The intuitions behind these designs are usually driven by the domain knowledge the designer has about the problem, along with some trial and error...
Hands-On Transfer Learning with Python
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Hands-On Transfer Learning with Python
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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.
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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
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