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Deep Learning

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

The Deep Learning course provides you with the knowledge and skills to excel in this rapidly advancing field. Explore the foundational principles of deep learning, including neural networks, activation functions, and backpropagation and gain a solid understanding of the mathematical concepts behind deep learning architectures. This course helps you explore advanced techniques such as transfer learning and leverage pre-trained models to accelerate your deep learning projects and understand how to adapt existing models to new tasks effectively.

Skills You’ll Get

Download Course Outline

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

1

Getting and Using Python

  • Exploring Jupyter Notebook
  • Understanding Cells of Jupyter Notebook
  • Understanding Indentation and Adding Comments in a Notebook
2

Leveraging a Deep Learning Framework

3

Reviewing Matrix Math and Optimization

  • Working with Matrices
4

Laying Linear Regression Foundations

  • Analyzing Data Using Linear Regression
  • Using Polynomial Expansion to Model Complex Relations
  • Analyzing Data Using Logistic Regression
5

Introducing Neural Networks

6

Building a Basic Neural Network

  • Creating a Neural Network Model
7

Explaining Convolutional Neural Networks

  • Building a LeNet5 Network
8

Performing Image Classification

  • Creating an Image Classifier Using CNNs
9

Working on Language Processing

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

Deep Learning

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