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Key benefits

- Practical recipes on training different neural network models and tuning them for optimal performance

-Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more

-A hands-on guide covering the common as well as the not so common problems in deep learning using Python

Description

Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics. The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.

Who is this book for?

This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired.

What you will learn

• Implement different neural network models in Python

• Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras

• Apply tips and tricks related to neural networks internals, to boost learning performances

• Consolidate machine learning principles and apply them in the deep learning field

• Reuse and adapt Python code snippets to everyday problems

• Evaluate the cost/benefits and performance implication of each discussed solution

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Gert Dec 30, 2017

positive points:• The book builds on simple recipes toward more complex recipes.• Great book if you want to learn by example!• A lot of useful code is included in the book that you can reuse for different projects.• What I like is that the author focusses on experimentation and doesn’t assume one method is better over another.negative points:• Sometimes a bit more explanation why some of the choices have been made would be good.• It would be better if the images are in colour (especially some charts).

Amazon Verified review

newby19 Nov 19, 2017

I think this book is a great way to get started using deep learning in a hands-on way. Especially for someone who's relatively new to deep learning and wants to experiment and work on different projects.Each recipe in the book is broken down to the different steps to take. Each step is described shortly and to the point. I used the recipes from the Computer Vision chapter right away to create my own image classification project.

Some Python Guy Dec 19, 2017

Found the code files on git but for example the wav/video files from chapter 9 are missing. Likely other source data files are missing. Please provide all relevant input files to run the samples.

Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

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