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Home
All Programs
Computers
Software Development & Engineering
General
Deep Learning
Deep Learning
Curriculum
25 Sections
90 Lessons
10 Weeks
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Introduction
5
1.1
About This Book
1.2
Foolish Assumptions
1.3
Icons Used in This Book
1.4
Beyond the Book
1.5
Where to Go from Here
Part 1: Discovering Deep Learning
0
Chapter 1: Introducing Deep Learning
4
3.1
Defining What Deep Learning Means
3.2
Using Deep Learning in the Real World
3.3
Considering the Deep Learning Programming Environment
3.4
Overcoming Deep Learning Hype
Chapter 2: Introducing the Machine Learning Principles
3
4.1
Defining Machine Learning
4.2
Considering the Many Different Roads to Learning
4.3
Pondering the True Uses of Machine Learning
Chapter 3: Getting and Using Python
8
5.1
Working with Python in this Book
5.2
Obtaining Your Copy of Anaconda
5.3
Downloading the Datasets and Example Code
5.4
Creating the Application
5.5
Understanding the Use of Indentation
5.6
Adding Comments
5.7
Getting Help with the Python Language
5.8
Working in the Cloud
Chapter 4: Leveraging a Deep Learning Framework
3
6.1
Presenting Frameworks
6.2
Working with Low-End Frameworks
6.3
Understanding TensorFlow
Part 2: Considering Deep Learning Basics
0
Chapter 5: Reviewing Matrix Math and Optimization
3
8.1
Revealing the Math You Really Need
8.2
Understanding Scalar, Vector, and Matrix Operations
8.3
Interpreting Learning as Optimization
Chapter 6: Laying Linear Regression Foundations
5
9.1
Combining Variables
9.2
Mixing Variable Types
9.3
Switching to Probabilities
9.4
Guessing the Right Features
9.5
Learning One Example at a Time
Chapter 7: Introducing Neural Networks
3
10.1
Discovering the Incredible Perceptron
10.2
Hitting Complexity with Neural Networks
10.3
Struggling with Overfitting
Chapter 8: Building a Basic Neural Network
2
11.1
Understanding Neural Networks
11.2
Looking Under the Hood of Neural Networks
Chapter 9: Moving to Deep Learning
5
12.1
Seeing Data Everywhere
12.2
Discovering the Benefits of Additional Data
12.3
Improving Processing Speed
12.4
Explaining Deep Learning Differences from Other Forms of AI
12.5
Finding Even Smarter Solutions
Chapter 10: Explaining Convolutional Neural Networks
3
13.1
Beginning the CNN Tour with Character Recognition
13.2
Explaining How Convolutions Work
13.3
Detecting Edges and Shapes from Images
Chapter 11: Introducing Recurrent Neural Networks
2
14.1
Introducing Recurrent Networks
14.2
Explaining Long Short-Term Memory
Part 3: Interacting with Deep Learning
0
Chapter 12: Performing Image Classification
2
16.1
Using Image Classification Challenges
16.2
Distinguishing Traffic Signs
Chapter 13: Learning Advanced CNNs
3
17.1
Distinguishing Classification Tasks
17.2
Perceiving Objects in Their Surroundings
17.3
Overcoming Adversarial Attacks on Deep Learning Applications
Chapter 14: Working on Language Processing
3
18.1
Processing Language
18.2
Memorizing Sequences that Matter
18.3
Using AI for Sentiment Analysis
Chapter 15: Generating Music and Visual Art
2
19.1
Learning to Imitate Art and Life
19.2
Mimicking an Artist
Chapter 16: Building Generative Adversarial Networks
2
20.1
Making Networks Compete
20.2
Considering a Growing Field
Chapter 17: Playing with Deep Reinforcement Learning
2
21.1
Playing a Game with Neural Networks
21.2
Explaining Alpha-Go
Part 4: The Part of Tens
0
Chapter 18: Ten Applications that Require Deep Learning
10
23.1
Restoring Color to Black-and-White Videos and Pictures
23.2
Approximating Person Poses in Real Time
23.3
Performing Real-Time Behavior Analysis
23.4
Translating Languages
23.5
Estimating Solar Savings Potential
23.6
Beating People at Computer Games
23.7
Generating Voices
23.8
Predicting Demographics
23.9
Creating Art from Real-World Pictures
23.10
Forecasting Natural Catastrophes
Chapter 19: Ten Must-Have Deep Learning Tools
10
24.1
Compiling Math Expressions Using Theano
24.2
Augmenting TensorFlow Using Keras
24.3
Dynamically Computing Graphs with Chainer
24.4
Creating a MATLAB-Like Environment with Torch
24.5
Performing Tasks Dynamically with PyTorch
24.6
Accelerating Deep Learning Research Using CUDA
24.7
Supporting Business Needs with Deeplearning4j
24.8
Mining Data Using Neural Designer
24.9
Training Algorithms Using Microsoft Cognitive Toolkit (CNTK)
24.10
Exploiting Full GPU Capability Using MXNet
Chapter 20: Ten Types of Occupations that Use Deep Learning
10
25.1
Managing People
25.2
Improving Medicine
25.3
Developing New Devices
25.4
Providing Customer Support
25.5
Seeing Data in New Ways
25.6
Performing Analysis Faster
25.7
Creating a Better Work Environment
25.8
Researching Obscure or Detailed Information
25.9
Designing Buildings
25.10
Enhancing Safety
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