Deep Learning for Practitioners (8-week evening course)

Course Description

This part-time evening course provides a practical approach for Deep Learning for practitioners, aka artificial neural networks. In this class, we will cover the fundamental concepts in mathematics and programming required to apply Deep Learning and AI. Students will apply these techniques to real-world problems, such as image classification, voice recognition, etc.

In this course, you will:

  • Apply Deep Learning and AI models to solve real world problems

  • Build a 3-layer artificial neural network from scratch

  • Explain and implement backpropagation algorithm

  • Build the following neural network architectures:

    • Convolutional Neural Networks (CNN)

    • Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)

    • Generative Adversarial Nets (GANs)

    • Reinforcement Learning (RL)

This course is ideal for students who:

  • Have a background in Mathematics (calculus, derivatives, basic linear algebra)

  • Are familiar with Data Exploration, basic Feature Engineering, and Statistics

  • Are familiar with the basic fundamentals of Machine Learning models

Class Structure

This course is an “active” learning environment. You’ll learn through doing. The focus will be on explaining concepts in your words and applying concepts through programming.

Course Schedule

This is a 8-week course. Classes run September 18 – November 15, 2017 (off the week of August 28). Classes meet twice per week – on Mondays and Wednesdays from 6pm-9pm PDT.