(DEEP-LEARNING.AE1) / ISBN : 978-1-64459-507-7
This course includes
Lessons
Hands-On Labs
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Lessons

21+ Lessons | 52+ Flashcards | 52+ Glossary of terms

Hands-On Labs

12+ LiveLab | 12+ Video tutorials | 21+ Minutes

1

Introduction

  • About This Course
  • Icons Used in This Course
  • Where to Go from Here
2

Introducing Deep Learning

  • Defining What Deep Learning Means
  • Using Deep Learning in the Real World
  • Considering the Deep Learning Programming Environment
  • Overcoming Deep Learning Hype
3

Introducing the Machine Learning Principles

  • Defining Machine Learning
  • Considering the Many Different Roads to Learning
  • Pondering the True Uses of Machine Learning
4

Getting and Using Python

  • Working with Python in this Course
  • Obtaining Your Copy of Anaconda
  • Downloading the Datasets and Example Code
  • Creating the Application
  • Understanding the Use of Indentation
  • Adding Comments
  • Getting Help with the Python Language
  • Working in the Cloud
5

Leveraging a Deep Learning Framework

  • Presenting Frameworks
  • Working with Low-End Frameworks
  • Understanding TensorFlow
6

Reviewing Matrix Math and Optimization

  • Revealing the Math You Really Need
  • Understanding Scalar, Vector, and Matrix Operations
  • Interpreting Learning as Optimization
7

Laying Linear Regression Foundations

  • Combining Variables
  • Mixing Variable Types
  • Switching to Probabilities
  • Guessing the Right Features
  • Learning One Example at a Time
8

Introducing Neural Networks

  • Discovering the Incredible Perceptron
  • Hitting Complexity with Neural Networks
  • Struggling with Overfitting
9

Building a Basic Neural Network

  • Understanding Neural Networks
  • Looking Under the Hood of Neural Networks
10

Moving to Deep Learning

  • Seeing Data Everywhere
  • Discovering the Benefits of Additional Data
  • Improving Processing Speed
  • Explaining Deep Learning Differences from Other Forms of AI
  • Finding Even Smarter Solutions
11

Explaining Convolutional Neural Networks

  • Beginning the CNN Tour with Character Recognition
  • Explaining How Convolutions Work
  • Detecting Edges and Shapes from Images
12

Introducing Recurrent Neural Networks

  • Introducing Recurrent Networks
  • Explaining Long Short-Term Memory
13

Performing Image Classification

  • Using Image Classification Challenges
  • Distinguishing Traffic Signs
14

Learning Advanced CNNs

  • Distinguishing Classification Tasks
  • Perceiving Objects in Their Surroundings
  • Overcoming Adversarial Attacks on Deep Learning Applications
15

Working on Language Processing

  • Processing Language
  • Memorizing Sequences that Matter
  • Using AI for Sentiment Analysis
16

Generating Music and Visual Art

  • Learning to Imitate Art and Life
  • Mimicking an Artist
17

Building Generative Adversarial Networks

  • Making Networks Compete
  • Considering a Growing Field
18

Playing with Deep Reinforcement Learning

  • Playing a Game with Neural Networks
  • Explaining Alpha-Go
19

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
20

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
21

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

3

Getting and Using Python

  • Exploring Jupyter Notebook
  • Understanding Cells of Jupyter Notebook
  • Understanding Indentation and Adding Comments in a Notebook
4

Leveraging a Deep Learning Framework

5

Reviewing Matrix Math and Optimization

  • Working with Matrices
6

Laying Linear Regression Foundations

  • Analyzing Data Using Linear Regression
  • Using Polynomial Expansion to Model Complex Relations
  • Analyzing Data Using Logistic Regression
7

Introducing Neural Networks

8

Building a Basic Neural Network

  • Creating a Neural Network Model
10

Explaining Convolutional Neural Networks

  • Building a LeNet5 Network
12

Performing Image Classification

  • Creating an Image Classifier Using CNNs
14

Working on Language Processing

  • Processing Text Using NLP
  • Building a Sentiment Analysis Algorithm Using RNNs

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