Neural Networks: Essentials
This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.
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Those interested in learning about neural networks, general machine learning and data science techniques, and SAS software
Course structure
Neural Networks: Essentials
- Introduction.
- Multilayer perceptrons.
- Neural network modeling paradigm.
- Using a surrogate model to interpret neural network predictions.
- Other considerations.
Neural Network Details
- Parameter estimation.
- Numerical optimization methods.
- Regularization.
- Unbalanced data.
- SAS search optimizations (self-study).
Tuning a Neural Network
- Selecting hyperparameters with autotuning.
Introduction to Deep Learning
- Introduction to deep learning.
- Autoencoders.
Radial Basis Function Networks (Self-Study)
Prerequisites
Before taking this course, you should have the following:
- Some familiarity with programming in SAS or SQL (or both).
- An understanding of predictive modeling.
- A basic understanding of calculus.
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