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Welcome to Neural Network Modeling. I’m Robert Blanchard. I teach Advanced Analytics at SAS. We’re at an exciting time because the rise of big data parallels impressive advancements in machine learning.

The neural network course you’re about to embark on will cover key machine learning concepts. You’ll learn theoretical and practical issues for fitting a neural network that include how to choose an appropriate neural network architecture and how to determine the relevant training method.

We’ll begin the course with an introduction to neural networks. Then we’ll discuss network architecture, followed by an examination of the learning process used by neural networks. This is an advanced course, but don’t worry. I’ll give you practical business tips whenever possible. Let’s get started.

Course Curriculum

Neural Network Modeling- Course Overview
Neural Network Modeling- Course Overview 00:00:00
Important notes about the Demonstrations and Exercises in this course File
Important notes about the Demonstrations and Exercises in this course File 00:00:00
1.1a- Introduction to Neural Networks- Introduction
1.1a- Introduction to Neural Networks- Introduction 00:00:00
1.1b- Introduction to Neural Networks- Objectives
1.1b- Introduction to Neural Networks- Objectives 00:00:00
1.2a- Biological Brain
1.2a- Biological Brain 00:00:00
1.2b- Types of Artificial Neural Networks
1.2b- Types of Artificial Neural Networks 00:00:00
1.2c- Artificial Neuron
1.2c- Artificial Neuron 00:00:00
1.2d- Learning Rules
1.2d- Learning Rules 00:00:00
1.2e- Perceptron
1.2e- Perceptron 00:00:00
1.2f- Truth Tables and Linear Separability
1.2f- Truth Tables and Linear Separability 00:00:00
1.2g- Generalized Delta Rule
1.2g- Generalized Delta Rule 00:00:00
1.2h- Mixture-of-Experts Networks
1.2h- Mixture-of-Experts Networks 00:00:00
1.3a- Modeling Nonlinearities with Traditional Methods
1.3a- Modeling Nonlinearities with Traditional Methods 00:00:00
1.3b- Nonlinear Regression
1.3b- Nonlinear Regression 00:00:00
1.3c- Demo Nonlinear Regression, Part 1
1.3c- Demo Nonlinear Regression, Part 1 00:00:00
1.3d- Demo Nonlinear Regression, Part 2
1.3d- Demo Nonlinear Regression, Part 2 00:00:00
1.3e- Nonparametric Regression
1.3e- Nonparametric Regression 00:00:00
1.3f- Demo Nonparametric Regression
1.3f- Demo Nonparametric Regression 00:00:00
1.3g- Limitations of Traditional Approaches
1.3g- Limitations of Traditional Approaches 00:00:00
1.4a- Deciding Whether to Use a Neural Network
1.4a- Deciding Whether to Use a Neural Network 00:00:00
1.4b- Advantages of Neural Networks
1.4b- Advantages of Neural Networks 00:00:00
1.4c- Disadvantages of Neural Networks
1.4c- Disadvantages of Neural Networks 00:00:00
1.4d- Demo Two-Spirals Problem
1.4d- Demo Two-Spirals Problem 00:00:00
2.1a- Network Architecture- Introduction
2.1a- Network Architecture- Introduction 00:00:00
2.1b-Network Architecture- Objectives
2.1b-Network Architecture- Objectives 00:00:00
2.2a- Linear Perceptron
2.2a- Linear Perceptron 00:00:00
2.2b- Demo Modeling Assorted Target Distributions, Part 1
2.2b- Demo Modeling Assorted Target Distributions, Part 1 00:00:00
2.2c- Demo Modeling Assorted Target Distributions, Part 2
2.2c- Demo Modeling Assorted Target Distributions, Part 2 00:00:00
2.3a- Multilayer Perceptron with a Single Hidden Layer
2.3a- Multilayer Perceptron with a Single Hidden Layer 00:00:00
2.3b- Multilayer Perceptron with Multiple Hidden Layers
2.3b- Multilayer Perceptron with Multiple Hidden Layers 00:00:00
2.3c- Sigmoid
2.3c- Sigmoid 00:00:00
2.3d- Optimal Number of Hidden Units
2.3d- Optimal Number of Hidden Units 00:00:00
2.3e- Demo Compositional Data, Part 1
2.3e- Demo Compositional Data, Part 1 00:00:00
2.3f- Demo Compositional Data, Part 2
2.3f- Demo Compositional Data, Part 2 00:00:00
2.3h- Exercise Constructing a Multilayer Perceptron Using PROC NLIN
2.3h- Exercise Constructing a Multilayer Perceptron Using PROC NLIN 00:00:00
2.3h- Exercise Solution- Constructing a Multilayer Perceptron Using PROC NLIN
2.3h- Exercise Solution- Constructing a Multilayer Perceptron Using PROC NLIN 00:00:00
2.4a- Overview of Radial Basis Functions
2.4a- Overview of Radial Basis Functions 00:00:00
2.4b- Inside the Radial Basis Function
2.4b- Inside the Radial Basis Function 00:00:00
2.4c- Ordinary Radial Basis Function2.4c- Ordinary Radial Basis Function
2.4c- Ordinary Radial Basis Function 00:00:00
2.4d- Normalized Radial Basis Function
2.4d- Normalized Radial Basis Function 00:00:00
2.4e- Demo Plotting Basis Functions
2.4e- Demo Plotting Basis Functions 00:00:00
2.4g- Exercise Comparing Architectures
2.4g- Exercise Comparing Architectures 00:00:00
2.4g- Exercise Solution- Comparing Architectures
2.4g- Exercise Solution- Comparing Architectures 00:00:00
3.1a- Learning- Introduction
3.1a- Learning- Introduction 00:00:00
3.1b- Learning- Objectives
3.1b- Learning- Objectives 00:00:00
3.2a- Global and Local Minimal
3.2a- Global and Local Minimal 00:00:00
3.2b- Initialization Procedure
3.2b- Initialization Procedure 00:00:00
3.2c- Preliminary Training
3.2c- Preliminary Training 00:00:00
3.2d- Convergence Criteria
3.2d- Convergence Criteria 00:00:00
3.2e- Improving Generalization
3.2e- Improving Generalization 00:00:00
3.2f- Demo Early Stopping, Part 1
3.2f- Demo Early Stopping, Part 1 00:00:00
3.2g- Demo Early Stopping, Part 2
3.2g- Demo Early Stopping, Part 2 00:00:00
3.2i- Exercise Network Initialization
3.2i- Exercise Network Initialization 00:00:00
3.2i- Exercise Solution Network Initialization
3.2i- Exercise Solution Network Initialization 00:00:00
3.3a- Ordinary Least Squares Estimation
3.3a- Ordinary Least Squares Estimation 00:00:00
3.3b- Maximum Likelihood Estimation
3.3b- Maximum Likelihood Estimation 00:00:00
3.3c- Deviance
3.3c- Deviance 00:00:00
3.3d- Robust Estimation and Appropriate Network Function Combinations
3.3d- Robust Estimation and Appropriate Network Function Combinations 00:00:00
3.3e- Demo Robust Networks
3.3e- Demo Robust Networks 00:00:00
3.3g- Exercise Modeling Count Data
3.3g- Exercise Modeling Count Data 00:00:00
3.3g- Exercise SOlution Modeling Count Data
3.3g- Exercise SOlution Modeling Count Data 00:00:00
3.4a- Iterative Updating
3.4a- Iterative Updating 00:00:00
3.4b- Propagation Optimization Methods
3.4b- Propagation Optimization Methods 00:00:00
3.4c- Other Optimization Methods
3.4c- Other Optimization Methods 00:00:00
3.4d- Demo Training Tracks
3.4d- Demo Training Tracks 00:00:00
4.1a- The Neural Procedure- Introduction
4.1a- The Neural Procedure- Introduction 00:00:00
4.1b- The Neural Procedure- Objectives
4.1b- The Neural Procedure- Objectives 00:00:00
4.2a- Using PROC NEURAL
4.2a- Using PROC NEURAL 00:00:00
4.2b- Demo Linear Perceptrons Using PROC NEURAL
4.2b- Demo Linear Perceptrons Using PROC NEURAL 00:00:00
4.3a- Using Weights to Select Useful Inputs
4.3a- Using Weights to Select Useful Inputs 00:00:00
4.3b- Demo Variable Importance
4.3b- Demo Variable Importance 00:00:00
4.3c- Sensitivity-Based Pruning
4.3c- Sensitivity-Based Pruning 00:00:00
4.3d- Demo Sensitivity-Based Pruning
4.3d- Demo Sensitivity-Based Pruning 00:00:00
4.3f- Exercise Sensitivity-Based Pruning (Bernoulli Distributed Target)
4.3f- Exercise Sensitivity-Based Pruning (Bernoulli Distributed Target) 00:00:00
4.3f- Exercise Solution Sensitivity-Based Pruning (Bernoulli Distributed Target)
4.3f- Exercise Solution Sensitivity-Based Pruning (Bernoulli Distributed Target) 00:00:00
4.4a- Sequential Network Construction
4.4a- Sequential Network Construction 00:00:00
4.4b- Demo Sequential Network Construction
4.4b- Demo Sequential Network Construction 00:00:00
4.4d- Exercise Sequential Network Construction
4.4d- Exercise Sequential Network Construction 00:00:00
4.4d- Exercise Solution Sequential Network Construction
4.4d- Exercise Solution Sequential Network Construction 00:00:00
4.5a- Self Study
4.5a- Self Study 00:00:00
4.6a- Gradient Descent
4.6a- Gradient Descent 00:00:00
4.6b- Demo Mini-Batch Gradient Descent, Part 1
4.6b- Demo Mini-Batch Gradient Descent, Part 1 00:00:00
4.6c- Demo Mini-Batch Gradient Descent, Part 2
4.6c- Demo Mini-Batch Gradient Descent, Part 2 00:00:00
5.1a-Augmented Networks- Introduction
5.1a-Augmented Networks- Introduction 00:00:00
5.1b-Augmented Networks Objectives
5.1b-Augmented Networks Objectives 00:00:00
5.2a- Time Series
5.2a- Time Series 00:00:00
5.2b- Neural Networks for Time Series
5.2b- Neural Networks for Time Series 00:00:00
5.2c- Long Short-Term Memory
5.2c- Long Short-Term Memory 00:00:00
5.2d- Reservoir Computing
5.2d- Reservoir Computing 00:00:00
5.2e- Demo Implementing a Time Delay Neural Network, Part 1
5.2e- Demo Implementing a Time Delay Neural Network, Part 1 00:00:00
5.2f- Demo Implementing a Time Delay Neural Network, Part 2
5.2f- Demo Implementing a Time Delay Neural Network, Part 2 00:00:00
5.3a- Surrogate Models
5.3a- Surrogate Models 00:00:00
5.3b- Demo Interpreting a Neural Network with a Continuous Target
5.3b- Demo Interpreting a Neural Network with a Continuous Target 00:00:00
5.3c- Self-Study Demo Interpreting a Neural Network with a Categorical Target
5.3c- Self-Study Demo Interpreting a Neural Network with a Categorical Target 00:00:00
6.1a- Hp Neural Code- Introduction
6.1a- Hp Neural Code- Introduction 00:00:00
6.1b- Hp Neural Code- Objectives
6.1b- Hp Neural Code- Objectives 00:00:00
6.2a- Thriving in the Big Data Era
6.2a- Thriving in the Big Data Era 00:00:00
6.2b- SAS High-Performance Analytics
6.2b- SAS High-Performance Analytics 00:00:00
6.3a- HP Neural Node
6.3a- HP Neural Node 00:00:00
6.3b- Demo Comparing One- and Two-Layer Networks, Part 1
6.3b- Demo Comparing One- and Two-Layer Networks, Part 1 00:00:00
6.3c- Demo Comparing One- and Two-Layer Networks, Part 2
6.3c- Demo Comparing One- and Two-Layer Networks, Part 2 00:00:00
Chapter 7: The AutoNeural Node (Self Study)
Chapter 7: The AutoNeural Node (Self Study) 00:00:00
Course Notes: Neural Network Modeling File
Course Notes: Neural Network Modeling File 00:00:00

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