• Online, Self-Paced
Course Description

Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10-video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.

Learning Objectives

Explore the concept of artificial neural networks (ANNs) and components of neural networks, and examine the concept of learning and training samples used in supervised, unsupervised, and reinforcement learning in this 10-video course. Other topics covered in this course include network topologies, neuron activation mechanism, training sets, pattern recognition, and the need for gradient optimization procedure for machine learning. You will begin the course with an overview of ANN and its components, then examine the artificial network topologies that implement feedforward, recurrent, and linked networks. Take a look at the activation mechanism for neural networks, and the prominent learning samples that can be applied in neural networks. Next, compare supervised learning samples, unsupervised learning samples, and reinforcement learning samples, and then view training samples and the approaches to building them. Explore training sets and pattern recognition and, in the final tutorial, examine the need for gradient optimization in neural networks. The exercise involves listing neural network components, activation functions, learning samples, and gradient descent optimization algorithms.

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture