CIS537 - Deep Learning and Neural Networks

Course Description

In this course, you will investigate the fundamental components of machine learning that are used to build a neural network. You will then construct a neural network and train it on a simple data set to make predictions on new data. We then look at how a neural network can be adapted for image data by exploring convolutional networks. You will have the opportunity to explore a simple implementation of a convolutional neural network written in PyTorch, a deep learning platform. Finally, you will yet again adapt neural networks, this time for sequential data. Using a deep averaging network, you will implement a neural sequence model that analyzes product reviews to determine consumer sentiment.

These courses are required to be completed prior to starting this course:

• Problem-Solving with Machine Learning
• Estimating Probability Distributions
• Learning with Linear Classifiers
• Decision Trees and Model Selection
• Debugging and Improving Machine Learning Models
• Learning with Kernel Machines

Faculty Author

Benefits to the Learner

• Explore the inner workings of neural networks
• Construct and train a neural network for new prediction tasks
• Adapt neural networks to take advantage of specific properties of image data
• Adapt neural networks to take advantage of specific properties of sequence data

Target Audience

• Programmers
• Developers
• Data analysts
• Statisticians
• Data scientists
• Software engineers

Applies Towards the Following Certificates

• Machine Learning : Required Courses