Course Description

This block covers the fundamental topics of neural networks. Topics include different types of neural networks such as multilayer perceptrons, convolutional neural networks and recurrent neural networks, optimization algorithms and techniques for neural networks, and autoencoders.

Course Outline

Module 1: Introduction to Multilayer Perceptrons (MLP)
  • Module 1.1: Multilayer Perceptrons
  • Module 1.2: Building an MLP in Keras
Module 2: Optimization for Neural Networks
  • Module 2.1: Loss Functions for Regression and Classification
  • Module 2.2: Optimization Algorithms
Module 3: Autoencoders
  • Module 3.1: Autoencoders
  • Module 3.2: Building an Autoencoder in Keras
Module 4: Introduction to Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN)
  • Module 4.1: Introduction to CNN
  • Module 4.2: Introduction to RNN
  • Module 4.3: CNN Implementation
  • Module 4.4: RNN Implementation

Learner Outcomes

  • Compare and contrast neural networks such as MLP, CNN, and RNN
  • Identify appropriate loss functions for classification and regression problems
  • Describe different optimization algorithms for tuning weights in neural networks
  • Apply autoencoders for dimension reduction
  • Describe how neural networks in Python might be used for real-world applications
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