PACK | Python Deep Learning Projects (2018 EN)

Author: Matthew Lamons, Rahul Kumar, Abhishek Nagaraja
Full Title: Python Deep Learning Projects
Publisher: Packt Publishing (October 31, 2018)
Year: 2018
ISBN-13: 9781788997096 (978-1-78899-709-6)
ISBN-10: 1788997093
Pages: 472
Language: English
Genre: Educational: Deep Learning
File type: EPUB (True), Code Files
Quality: 10/10
Price: 35.99 €


Insightful projects to master deep learning and neural network architectures using Python and Keras.

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.

Python Deep Learning Projects imparts all the knowledge needed to implement complex deep learning projects in the field of computational linguistics and computer vision. Each of these projects is unique, helping you progressively master the subject. You’ll learn how to implement a text classifier system using a recurrent neural network (RNN) model and optimize it to understand the shortcomings you might experience while implementing a simple deep learning system.

Similarly, you’ll discover how to develop various projects, including word vector representation, open domain question answering, and building chatbots using seq-to-seq models and language modeling. In addition to this, you’ll cover advanced concepts, such as regularization, gradient clipping, gradient normalization, and bidirectional RNNs, through a series of engaging projects.

By the end of this book, you will have gained knowledge to develop your own deep learning systems in a straightforward way and in an efficient way.


Learn:
✓ Set up a deep learning development environment on Amazon Web Services (AWS)
✓ Apply GPU-powered instances as well as the deep learning AMI
✓ Implement seq-to-seq networks for modeling natural language processing (NLP)
✓ Develop an end-to-end speech recognition system
✓ Build a system for pixel-wise semantic labeling of an image
✓ Create a system that generates images and their regions

Features:
✓ Explore deep learning across computer vision, natural language processing (NLP), and image processing
✓ Discover best practices for the training of deep neural networks and their deployment
✓ Access popular deep learning models as well as widely used neural network architectures

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