## Neural Networks: EssentialsHá uma nova versão para esse curso. Por favor, veja 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. The self-study e-learning includes: - Annotatable course notes in PDF format.
- Virtual lab time to practice.
Aprenda como
- 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.
## Quem poderá participarThose interested in learning about neural networks, general machine learning and data science techniques, and SAS software
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.
Este curso aborda SAS Viya software. 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)INTN35 |
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