Book description
Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide
About This Book
 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 handson guide covering the common as well as the not so common problems in deep learning using Python
Who This Book Is For
This book is intended for machine learning professionals who are looking to use deep learning algorithms to create realworld applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikitlearn 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
In Detail
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 topdown and bottomup approach to demonstrate deep learning solutions to realworld 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 notsocommon scenarios.
Style and approach
Unique blend of independent recipes arranged in the most logical manner
Publisher resources
Table of contents
 Preface

Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks
 Introduction
 Setting up a deep learning environment
 Launching an instance on Amazon Web Services (AWS)
 Launching an instance on Google Cloud Platform (GCP)
 Installing CUDA and cuDNN
 Installing Anaconda and libraries
 Connecting with Jupyter Notebooks on a server
 Building stateoftheart, productionready models with TensorFlow
 Intuitively building networks with Keras
 Using PyTorch’s dynamic computation graphs for RNNs
 Implementing highperformance models with CNTK
 Building efficient models with MXNet
 Defining networks using simple and efficient code with Gluon

FeedForward Neural Networks
 Introduction
 Understanding the perceptron
 Implementing a singlelayer neural network
 Building a multilayer neural network
 Getting started with activation functions
 Experiment with hidden layers and hidden units
 Implementing an autoencoder
 Tuning the loss function
 Experimenting with different optimizers
 Improving generalization with regularization
 Adding dropout to prevent overfitting
 Convolutional Neural Networks
 Recurrent Neural Networks
 Reinforcement Learning
 Generative Adversarial Networks
 Computer Vision
 Natural Language Processing
 Speech Recognition and Video Analysis
 Time Series and Structured Data
 Game Playing Agents and Robotics

Hyperparameter Selection, Tuning, and Neural Network Learning
 Introduction
 Visualizing training with TensorBoard and Keras
 Working with batches and minibatches
 Using grid search for parameter tuning
 Learning rates and learning rate schedulers
 Comparing optimizers
 Determining the depth of the network
 Adding dropouts to prevent overfitting
 Making a model more robust with data augmentation
 Network Internals
 Pretrained Models
Product information
 Title: Python Deep Learning Cookbook
 Author(s):
 Release date: October 2017
 Publisher(s): Packt Publishing
 ISBN: 9781787125193
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