CCNA 200-301 Pearson uCertify Network Simulator
Cisco 200-301-SIMULATOR.AB1
CCNA 200-301 Pearson uCertify Network Simulator
ISBN: 978-1-61691-837-8Cisco 200-301-SIMULATOR.AB1
Deep Learning
(DEEP-LEARNING.AE1) / ISBN : 978-1-64459-507-7
This course includes
Interactive Lessons
Hands-On Labs
About This Course
Skills You’ll Get
Interactive Lessons
21+ Interactive 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
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
