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
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
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
