Neural Networks: Essentials

Course code: 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.

The self-study e-learning includes:

Annotatable course notes in PDF format.
Virtual lab time to practice.

720 EUR

871 EUR including VAT

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Prerequisites

Course dates

Starting date: Upon request

Type: E-learning

Course duration: 21 hours

Language: en

Price without VAT: 720 EUR

Starting date: Upon request

Type: Upon request

Course duration: 10h 30min

Language: en

Price without VAT: 1 200 EUR

	Starting date	Place	Type	Course duration	Language	Price without VAT
	Upon request		E-learning	21 hours	en	720 EUR	Register

	Upon request		Upon request	10h 30min	en	1 200 EUR	Register
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Target group

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.

Do you need advice or a tailor-made course?

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+420 731 175 867

edu@edutrainings.cz