Course details

Build & Train RNNs: Neural Network Components

Build & Train RNNs: Neural Network Components


Overview/Description
Expected Duration
Lesson Objectives
Course Number
Expertise Level



Overview/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.



Expected Duration (hours)
0.6

Lesson Objectives

Build & Train RNNs: Neural Network Components

  • Course Overview
  • describe artificial neural network and its components
  • identify the topology of the networks that implements feedforward, recurrent and linked networks
  • list activation mechanisms used in the implementation of neural networks
  • specify the prominent learning samples that can be applied in neural networks
  • compare Supervised, Unsupervised, and Reinforcement learning samples
  • describe training samples and the approaches for building them
  • identify training sets and recognize patterns
  • recognize the need for gradient optimization in neural networks
  • list neural network components, activation functions, learning samples, and gradient descent optimization algorithms
  • Course Number:
    it_mlbtrndj_01_enus

    Expertise Level
    Intermediate