Book Image

Python Deep Learning Cookbook

By : Indra den Bakker
Book Image

Python Deep Learning Cookbook

By: Indra den Bakker

Overview of this book

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.

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Table of Contents (21 chapters)
Title Page
Title Page
Credits
Credits
About the Author
About the Author
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks
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 state-of-the-art, production-ready models with TensorFlow
Intuitively building networks with Keras 
Using PyTorch’s dynamic computation graphs for RNNs
Implementing high-performance models with CNTK
Building efficient models with MXNet
Defining networks using simple and efficient code with Gluon
2
Feed-Forward Neural Networks
Feed-Forward Neural Networks
Introduction
Understanding the perceptron
Implementing a single-layer neural network
Building a multi-layer 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
3
Convolutional Neural Networks
Convolutional Neural Networks
Introduction
Applying pooling layers
Optimizing with batch normalization
Understanding padding and strides
Experimenting with different types of initialization
Implementing a convolutional autoencoder
Applying a 1D CNN to text
4
Recurrent Neural Networks
Recurrent Neural Networks
Introduction
Implementing a simple RNN
Adding Long Short-Term Memory (LSTM)
Using gated recurrent units (GRUs)
Implementing bidirectional RNNs
Character-level text generation
5
Reinforcement Learning
Reinforcement Learning
Introduction
Implementing policy gradients
Implementing a deep Q-learning algorithm
6
Generative Adversarial Networks
Generative Adversarial Networks
Introduction
Understanding GANs
Implementing Deep Convolutional GANs (DCGANs) 
Upscaling the resolution of images with Super-Resolution GANs (SRGANs)
7
Computer Vision
Computer Vision
Introduction
Augmenting images with computer vision techniques
Classifying objects in images
Localizing an object in images
Segmenting classes in images with U-net
Scene understanding (semantic segmentation)
Finding facial key points
Recognizing faces
Transferring styles to images
8
Natural Language Processing
Natural Language Processing
Introduction
Analyzing sentiment
Translating sentences
Summarizing text
9
Speech Recognition and Video Analysis
Speech Recognition and Video Analysis
Introduction
Implementing a speech recognition pipeline from scratch
Identifying speakers with voice recognition
Understanding videos with deep learning
10
Time Series and Structured Data
Time Series and Structured Data
Introduction
Predicting stock prices with neural networks
Predicting bike sharing demand
Using a shallow neural network for binary classification
11
Game Playing Agents and Robotics
Game Playing Agents and Robotics
Introduction
Learning to drive a car with end-to-end learning
Learning to play games with deep reinforcement learning
Genetic Algorithm (GA) to optimize hyperparameters
12
Hyperparameter Selection, Tuning, and Neural Network Learning
Hyperparameter Selection, Tuning, and Neural Network Learning
Introduction
Visualizing training with TensorBoard and Keras
Working with batches and mini-batches
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
13
Network Internals
Network Internals
Introduction
Visualizing training with TensorBoard
Analyzing network weights and more
Freezing layers
Storing the network topology and trained weights
14
Pretrained Models
Pretrained Models
Introduction
Large-scale visual recognition with GoogLeNet/Inception
Extracting bottleneck features with ResNet
Leveraging pretrained VGG models for new classes
Fine-tuning with Xception

About the Reviewer

Radovan Kavicky is the principal data scientist and President at GapData Institute (https://www.gapdata.org) based in Bratislava, Slovakia, where he harnesses the power of data and the wisdom of economics for public good.

A macroeconomist by education/academic background and a consultant and analyst by profession (with more than 8 years of experience in consulting for clients from the public and private sector), with strong mathematical and analytical skills, he is able to deliver top-level research and analytical work. From MATLAB, SAS, and Stata, he switched to Python, R, and Tableau.

He is a member of the Slovak Economic Association and an evangelist of open data, open budget initiatives, and open government partnership. He is the founder of PyData Bratislava, R <- Slovakia, and SK/CZ Tableau User Group (skczTUG). He has been a speaker at TechSummit (Bratislava, 2017) and at PyData (Berlin, 2017), and a member of the global Tableau #DataLeader network (2017). You can follow him on Twitter at @radovankavicky, @GapDataInst, or @PyDataBA. His full profile and experience is available at https://www.linkedin.com/in/radovankavicky/ and https://github.com/radovankavicky.

 

 

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