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Interest in Deep Learning has been growing in the past few years. With advances in software and hardware technologies, Neural Networks are making a resurgence. With interest in AI based applications growing, and companies like IBM, Google, Microsoft, NVidia investing heavily in computing and software applications, it is time to understand Deep Learning better!
In this lecture, we will discuss the basics of Neural Networks and discuss how Deep Learning Neural networks are different from conventional Neural Network architectures. We will review a bit of mathematics that goes into building neural networks and understand the role of GPUs in Deep Learning. We will also get an introduction to Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks and understand the state-of-the-art in hardware and software architectures. Functional Demos will be presented in Keras, a popular Python package with a backend in Theano. This will be a preview of the QuantUniversity Deep Learning Workshop that will be offered in 2017.
December 21st 2016
Deep Learning : An introduction
2016 Copyright QuantUniversity LLC.
Sri Krishnamurthy, CFA, CAP
Slides and Code will be available at:
- Analytics Advisory services
- Custom training programs
- Architecture assessments, advice and audits
• Founder of QuantUniversity LLC. and
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Founder and CEO
Quantitative Analytics and Big Data Analytics Onboarding
• Trained more than 500 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Launching the Analytics Certificate
Program in September
• January 2017
▫ 19th, Deep Learning Lecture Part II
• February 2017
▫ Deep Learning Workshop (Date TBD)
Events of Interest
Dr. Victor Shnayder
Product Manager EdX (March 2013-June 2016)
PhD, Computer Science
1. Our labeled datasets were thousands of times too small.
2. Our computers were millions of times too slow.
3. We initialized the weights in a stupid way.
4. We used the wrong type of non-linearity.
- Geoff Hinton
Neural nets were tried in the 1980s. What changed?
• Theano is a Python library that allows you to define, optimize, and
evaluate mathematical expressions involving multi-dimensional
• Performs efficient symbolic differentiation
• Leverages NVIDIA GPU (Claim 140X faster than CPU)
• Developed by University of Montreal researchers and is open-source
• Works on Windows/Linux/Mac OS
• See https://arxiv.org/abs/1605.02688
• GPU vs CPU
▫ Theano Test
▫ See Theano Test.ipyb
• Logistic Regression
See Theano-Logistic Regression.ipyb
• Keras is a high-level neural networks library, written in Python and
capable of running on top of either TensorFlow or Theano. It was
developed with a focus on enabling fast experimentation.
• Allows for easy and fast prototyping (through total modularity,
minimalism, and extensibility).
• Supports both convolutional networks and recurrent networks, as
well as combinations of the two.
• Supports arbitrary connectivity schemes (including multi-input and
• Runs seamlessly on CPU and GPU.
• Keras Examples
▫ Testing Keras: See KerasPython.ipynb
▫ Running Convolutional NN on Keras with a Theano Backend
• A case study for Convolutional Neural Networks
• Recurrent Neural Networks
• Auto Encoders
• Best Practices
Coming on January 21st - Part II
Members & Sponsors!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.