Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

ISBN: 1788831306

Language: English

Category: Uncategorized

Posted on 2019-05-10, by manhneovn.


English | (August 31, 2018) | ISBN: 1788831306 | 438 pages | EPUB | 46.15 MB

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Key Features
Build deep learning models with transfer learning principles in Python
implement transfer learning to solve real-world research problems
Perform complex operations such as image captioning neural style transfer
Book Description
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.

What you will learn
Set up your own DL environment with graphics processing unit (GPU) and Cloud support
Delve into transfer learning principles with ML and DL models
Explore various DL architectures, including CNN, LSTM, and capsule networks
Learn about data and network representation and loss functions
Get to grips with models and strategies in transfer learning
Walk through potential challenges in building complex transfer learning models from scratch
Explore real-world research problems related to computer vision and audio analysis
Understand how transfer learning can be leveraged in NLP
Who this book is for
Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.


Sponsored High Speed Downloads
9356 dl's @ 2739 KB/s
Download Now [Full Version]
5100 dl's @ 3358 KB/s
Download Link 1 - Fast Download
8507 dl's @ 3233 KB/s
Download Mirror - Direct Download

Search More...
Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Search free ebooks in ebookee.com!

Download this book

No active download links here?
Please check the description for download links if any or do a search to find alternative books.

Related Books


No comments for "Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras".

    Add Your Comments
    1. Download links and password may be in the description section, read description carefully!
    2. Do a search to find mirrors if no download links or dead links.
    Back to Top