Deep Learning For Dummies

Title Page

Copyright Page

Table of Contents

Introduction

About This Book

Foolish Assumptions

Icons Used in This Book

Beyond the Book

Where to Go from Here

Part 1 Discovering Deep Learning

Chapter 1 Introducing Deep Learning

Defining What Deep Learning Means

Starting from Artificial Intelligence

Considering the role of AI

Focusing on machine learning

Moving from machine learning to deep learning

Using Deep Learning in the Real World

Understanding the concept of learning

Performing deep learning tasks

Employing deep learning in applications

Considering the Deep Learning Programming Environment

Overcoming Deep Learning Hype

Discovering the start-up ecosystem

Knowing when not to use deep learning

Chapter 2 Introducing the Machine Learning Principles

Defining Machine Learning

Understanding how machine learning works

Understanding that it’s pure math

Learning by different strategies

Training, validating, and testing data

Looking for generalization

Getting to know the limits of bias

Keeping model complexity in mind

Considering the Many Different Roads to Learning

Understanding there is no free lunch

Discovering the five main approaches

Delving into some different approaches

Awaiting the next breakthrough

Pondering the True Uses of Machine Learning

Understanding machine learning benefits

Discovering machine learning limits

Chapter 3 Getting and Using Python

Working with Python in this Book

Obtaining Your Copy of Anaconda

Getting Continuum Analytics Anaconda

Installing Anaconda on Linux

Installing Anaconda on MacOS

Installing Anaconda on Windows

Downloading the Datasets and Example Code

Using Jupyter Notebook

Defining the code repository

Getting and using datasets

Creating the Application

Understanding cells

Adding documentation cells

Using other cell types

Understanding the Use of Indentation

Adding Comments

Understanding comments

Using comments to leave yourself reminders

Using comments to keep code from executing

Getting Help with the Python Language

Working in the Cloud

Using the Kaggle datasets and kernels

Using the Google Colaboratory

Chapter 4 Leveraging a Deep Learning Framework

Presenting Frameworks

Defining the differences

Explaining the popularity of frameworks

Defining the deep learning framework

Choosing a particular framework

Working with Low-End Frameworks

Caffe2

Chainer

PyTorch

MXNet

Microsoft Cognitive Toolkit/CNTK

Understanding TensorFlow

Grasping why TensorFlow is so good

Making TensorFlow easier by using TFLearn

Using Keras as the best simplifier

Getting your copy of TensorFlow and Keras

Fixing the C++ build tools error in Windows

Accessing your new environment in Notebook

Part 2 Considering Deep Learning Basics

Chapter 5 Reviewing Matrix Math and Optimization

Revealing the Math You Really Need

Working with data

Creating and operating with a matrix

Understanding Scalar, Vector, and Matrix Operations

Creating a matrix

Performing matrix multiplication

Executing advanced matrix operations

Extending analysis to tensors

Using vectorization effectively

Interpreting Learning as Optimization

Exploring cost functions

Descending the error curve

Learning the right direction

Updating

Chapter 6 Laying Linear Regression Foundations

Combining Variables

Working through simple linear regression

Advancing to multiple linear regression

Including gradient descent

Seeing linear regression in action

Mixing Variable Types

Modeling the responses

Modeling the features

Dealing with complex relations

Switching to Probabilities

Specifying a binary response

Transforming numeric estimates into probabilities

Guessing the Right Features

Defining the outcome of incompatible features

Solving overfitting using selection and regularization

Learning One Example at a Time

Using gradient descent

Understanding how SGD is different

Chapter 7 Introducing Neural Networks

Discovering the Incredible Perceptron

Understanding perceptron functionality

Touching the nonseparability limit

Hitting Complexity with Neural Networks

Considering the neuron

Pushing data with feed-forward

Going even deeper into the rabbit hole

Using backpropagation to adjust learning

Struggling with Overfitting

Understanding the problem

Opening the black box

Chapter 8 Building a Basic Neural Network

Understanding Neural Networks

Defining the basic architecture

Documenting the essential modules

Solving a simple problem

Looking Under the Hood of Neural Networks

Choosing the right activation function

Relying on a smart optimizer

Setting a working learning rate

Chapter 9 Moving to Deep Learning

Seeing Data Everywhere

Considering the effects of structure

Understanding Moore’s implications

Considering what Moore’s Law changes

Discovering the Benefits of Additional Data

Defining the ramifications of data

Considering data timeliness and quality

Improving Processing Speed

Leveraging powerful hardware

Making other investments

Explaining Deep Learning Differences from Other Forms of AI

Adding more layers

Changing the activations

Adding regularization by dropout

Finding Even Smarter Solutions

Using online learning

Transferring learning

Learning end to end

Chapter 10 Explaining Convolutional Neural Networks

Beginning the CNN Tour with Character Recognition

Understanding image basics

Explaining How Convolutions Work

Understanding convolutions

Simplifying the use of pooling

Describing the LeNet architecture

Detecting Edges and Shapes from Images

Visualizing convolutions

Unveiling successful architectures

Discussing transfer learning

Chapter 11 Introducing Recurrent Neural Networks

Introducing Recurrent Networks

Modeling sequences using memory

Recognizing and translating speech

Placing the correct caption on pictures

Explaining Long Short-Term Memory

Defining memory differences

Walking through the LSTM architecture

Discovering interesting variants

Getting the necessary attention

Part 3 Interacting with Deep Learning

Chapter 12 Performing Image Classification

Using Image Classification Challenges

Delving into ImageNet and MS COCO

Learning the magic of data augmentation

Distinguishing Traffic Signs

Preparing image data

Running a classification task

Chapter 13 Learning Advanced CNNs

Distinguishing Classification Tasks

Performing localization

Classifying multiple objects

Annotating multiple objects in images

Segmenting images

Perceiving Objects in Their Surroundings

Discovering how RetinaNet works

Using the Keras-RetinaNet code

Overcoming Adversarial Attacks on Deep Learning Applications

Tricking pixels

Hacking with stickers and other artifacts

Chapter 14 Working on Language Processing

Processing Language

Defining understanding as tokenization

Putting all the documents into a bag

Memorizing Sequences that Matter

Understanding semantics by word embeddings

Using AI for Sentiment Analysis

Chapter 15 Generating Music and Visual Art

Learning to Imitate Art and Life

Transferring an artistic style

Reducing the problem to statistics

Understanding that deep learning doesn’t create

Mimicking an Artist

Defining a new piece based on a single artist

Combining styles to create new art

Visualizing how neural networks dream

Using a network to compose music

Chapter 16 Building Generative Adversarial Networks

Making Networks Compete

Finding the key in the competition

Achieving more realistic results

Considering a Growing Field

Inventing realistic pictures of celebrities

Enhancing details and image translation

Chapter 17 Playing with Deep Reinforcement Learning

Playing a Game with Neural Networks

Introducing reinforcement learning

Simulating game environments

Presenting Q-learning

Explaining Alpha-Go

Determining if you’re going to win

Applying self-learning at scale

Part 4 The Part of Tens

Chapter 18 Ten Applications that Require Deep Learning

Restoring Color to Black-and-White Videos and Pictures

Approximating Person Poses in Real Time

Performing Real-Time Behavior Analysis

Translating Languages

Estimating Solar Savings Potential

Beating People at Computer Games

Generating Voices

Predicting Demographics

Creating Art from Real-World Pictures

Forecasting Natural Catastrophes

Chapter 19 Ten Must-Have Deep Learning Tools

Compiling Math Expressions Using Theano

Augmenting TensorFlow Using Keras

Dynamically Computing Graphs with Chainer

Creating a MATLAB-Like Environment with Torch

Performing Tasks Dynamically with PyTorch

Accelerating Deep Learning Research Using CUDA

Supporting Business Needs with Deeplearning4j

Mining Data Using Neural Designer

Training Algorithms Using Microsoft Cognitive Toolkit (CNTK)

Exploiting Full GPU Capability Using MXNet

Chapter 20 Ten Types of Occupations that Use Deep Learning

Managing People

Improving Medicine

Developing New Devices

Providing Customer Support

Seeing Data in New Ways

Performing Analysis Faster

Creating a Better Work Environment

Researching Obscure or Detailed Information

Designing Buildings

Enhancing Safety

Index

EULA