Python deep learning cookbook : over 75 practical recipes on neutral network modeling, reinforcement learning, and transfer learning using Python / Indra den Bakker.

Format:

Book

Author/Creator:

Bakker, Indra den, author.

Language:

English

Subjects (All):

Python (Computer program language).

Python (Computer program language)--Handbooks, manuals, etc.

Physical Description:

1 online resource (1 volume) : illustrations

Edition:

1st edition

Other Title:

Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python

Place of Publication:

Birmingham, England ; Mumbai, [India] : Packt, 2017.

System Details:

text file

Biography/History:

den Bakker Indra: Indra den Bakker is an experienced deep learning engineer and mentor. He is the founder of 23insightspart of NVIDIA's Inception programa machine learning start-up building solutions that transform the worlds most important industries. For Udacity, he mentors students pursuing a Nanodegree in deep learning and related fields, and he is also responsible for reviewing student projects. Indra has a background in computational intelligence and worked for several years as a data scientist for IPG Mediabrands and Screen6 before founding 23insights.

Summary:

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 hands-on 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 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 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 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. Style and approach Unique blend of independent recipes arranged in the most logical manner

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks

Introduction

Setting up a deep learning&amp

#160

environment

How to do it...

Launching an instance on Amazon Web Services (AWS)

Getting ready

Launching an instance on Google Cloud Platform (GCP)

Installing CUDA and&amp

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&amp

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

Chapter 2: Feed-Forward Neural Networks

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

There's more...

Implementing an autoencoder

Tuning the loss function

Experimenting with different optimizers

Improving generalization with regularization

Adding dropout to prevent overfitting

How to do it.

Chapter 3: Convolutional Neural Networks

Getting started with filters and parameter sharing

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

Chapter 4: Recurrent Neural Networks

Implementing a simple RNN

Adding Long Short-Term Memory (LSTM)

Using gated recurrent units (GRUs)

Implementing bidirectional RNNs

Character-level text generation

Chapter 5: Reinforcement Learning

Implementing policy gradients

Implementing a deep Q-learning algorithm

Chapter 6: Generative Adversarial Networks

Understanding GANs

Implementing Deep Convolutional GANs&amp

(DCGANs)&amp

Upscaling the resolution of images with Super-Resolution GANs (SRGANs)

Chapter 7: Computer Vision

Augmenting images with computer vision techniques

Classifying objects in images

Localizing an object in images

Real-time detection frameworks

Segmenting classes in images with U-net

Scene understanding (semantic segmentation)

Finding facial key points

Recognizing faces

Transferring styles to images

Chapter 8: Natural Language Processing.

Introduction

Analyzing sentiment

Translating sentences

Summarizing text

Chapter 9: Speech Recognition and Video Analysis

Implementing a speech recognition pipeline from scratch

Identifying speakers with voice recognition

Understanding videos with deep learning

Chapter 10: Time Series and Structured Data

Predicting stock prices with neural networks

Predicting bike sharing demand

Using a shallow neural network for binary classification

Chapter 11: Game Playing Agents and Robotics

Learning to drive a car with end-to-end learning

Getting started

Learning to play games with deep reinforcement learning

Genetic Algorithm (GA) to optimize hyperparameters

How to do it..

Chapter 12: Hyperparameter Selection, Tuning, and Neural Network Learning

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

Leveraging test-time augmentation (TTA) to boost accuracy

Chapter 13: Network Internals

Visualizing training with TensorBoard

Visualizing the network architecture with TensorBoard

Analyzing network weights and more

Freezing layers

Storing the network topology and trained weights

How to do it...&amp

Chapter 14: Pretrained Models

Large-scale visual recognition with GoogLeNet/Inception

Extracting bottleneck features with ResNet

Leveraging pretrained VGG models for new classes

Fine-tuning with Xception

Index.

Notes:

Includes index.

Description based on online resource; title from PDF title page (EBC, viewed December 2, 2017).

ISBN:

9781787122253

1787122255

OCLC:

1015687240