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12-week evening course.
Mon, Jun 26, 2017 @ 06:00 PM   Not Known   Venue, 44 Tehama St
 
     
 
 
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<P><STRONG>Course Description</STRONG></P>
<P>This <EM>part-time evening course</EM> provides a broad overview of Deep Learning applications for practitioners, aka artificial neural networks. In this class, we will cover the fundamental concepts in mathematics & programming required to apply Deep Learning & AI. Students will apply these techniques to real-world problems, such as image classification, voice recognition, etc.</P>
<P><STRONG>In this course, you will:</STRONG></P>
<UL>
<LI>Apply Deep Learning & AI models to solve real world problems</LI>
<LI>Build a 3-layer artificial neural network from scratch</LI>
<LI>Explain & implement backpropagation algorithm</LI>
<LI>Build the following neural network architectures:
<UL>
<LI>Convolutional Neural Networks (CNN)</LI>
<LI>Recurrent Neural Network (RNN) & Long Short Term Memory (LSTM)</LI>
<LI>Generative Adversarial Nets (GANs)</LI>
<LI>Reinforcement Learning (RL)</LI>
</UL>
</LI>
</UL>
<P><STRONG>This course is ideal for students who:</STRONG></P>
<UL>
<LI>Have a background in Mathematics (calculus, derivatives, basic linear algebra)</LI>
<LI>Are familiar with Data Exploration, basic Feature Engineering, & Statistics</LI>
<LI>Are familiar with the basic fundamentals of Machine Learning models</LI>
</UL>
<P><STRONG>Class Structure</STRONG></P>
<P>This course is an active learning environment. You'll learn through doing. The focus will be on explaining concepts in your words & applying concepts through programming.</P>
<P><STRONG>Course Schedule</STRONG></P>
<P>This is a 12-week course. Classes run <STRONG><EM>June 26 - September 13. </EM></STRONG>Classes meet twice per week - on Mondays & Wednesdays from 6pm-9pm PDT.</P>
 
 
 
 
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