Neural Networks: Essentials
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Neural Networks: Essentials
Duration: 10.5 hours
INTNN : INTN35
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.
Learn How To
  • Programmatically build neural networks in SAS 9.4 and SAS Viya.
  • Modify neural networks' parameters for better performance.
  • Conduct automatic search for neural networks' hyperparameters through genetic algorithm.
  • Enhance data with autoencoders and synthetic observations.
  • Who Should Attend
    Those interested in learning about neural networks, general machine learning and data science techniques, and SAS software
    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.
  • SAS Products Covered
    SAS Viya
    Course Outline
    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)
  • THIS COURSE IS PART OF

    SAS AI and Machine Learning Professional Subscription



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