Sign In Start Free Trial

Book Overview & Buying
Table Of Contents

Python Deep Learning Cookbook

By : Indra den Bakker

3.7 (3)

Python Deep Learning Cookbook

3.7 (3)

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.

Preface

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Free Chapter

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

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

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

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

Reinforcement Learning

Reinforcement Learning

Introduction

Implementing policy gradients

Implementing a deep Q-learning algorithm

Generative Adversarial Networks

Generative Adversarial Networks

Introduction

Understanding GANs

Implementing Deep Convolutional GANs (DCGANs)

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

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

Natural Language Processing

Natural Language Processing

Introduction

Analyzing sentiment

Translating sentences

Summarizing text

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

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

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

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

Network Internals

Network Internals

Introduction

Visualizing training with TensorBoard

Analyzing network weights and more

Freezing layers

Storing the network topology and trained weights

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

Introduction

The recent advancements in deep learning can be, to some extent, attributed to the advancements in computing power. The increase in computing power, more specifically the use of GPUs for processing data, has contributed to the leap from shallow neural networks to deeper neural networks. In this chapter, we lay the groundwork for all following chapters by showing you how to set up stable environments for different deep learning frameworks used in this cookbook. There are many open source deep learning frameworks that are used by researchers and in the industry. Each framework has its own benefits and most of them are supported by some big tech company.

By following the steps in this first chapter carefully, you should be able to use local or cloud-based CPUs and GPUs to leverage the recipes in this book. For this book, we've used Jupyter Notebooks to execute all code blocks. These notebooks provide interactive feedback per code block in such a way that it's perfectly suited for storytelling.

The download links in this recipe are intended for an Ubuntu machine or server with a supported NVIDIA GPU. Please change the links and filenames accordingly if needed. You are free to use any other environment, package managers (for example, Docker containers), or versions if needed. However, additional steps may be required.

CONTINUE READING

83

Tech Concepts

36

Programming languages

73

Tech Tools

Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.

Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.

50+ new titles added per month and exclusive early access to books as they are being written.

Python Deep Learning Cookbook

Search

Your notes and bookmarks