Deep learning from scratch

Bruno Goncalves (Data For Science)

9:00am–12:30pm Tuesday, September 24, 2019

Location: 1E 12/13

Data Science, Machine Learning, & AI

Secondary topics: Deep Learning

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Who is this presentation for?

Data scientists, business analysts, data engineers, and machine learning researchers

Level

Intermediate

Description

Over the past few years, we’ve seen a convergence of two large-scale trends: big data and big compute. The resulting combination of large amounts of data and abundant CPU (and GPU) cycles has brought to the forefront and highlighted the power of neural network techniques and approaches that were once thought to be too impractical.

Deep learning, as this new wave of interest has come to be known, has made impressive and unprecedented progress on applications as diverse as natural language processing, machine translation, computer vision, robotics, etc. You’ll go hands-on to learn the theoretical foundations and principal ideas underlying deep learning and neural networks. Bruno Gonçalves provides the code structure of the implementations that closely resembles the way Keras is structured, so that by the end of the course, you’ll be prepared to dive deeper into the deep learning applications of your choice.

Prerequisite knowledge

Experience with Python, NumPy, and SciPy

Materials or downloads needed in advance

A laptop with a scientific Python distribution (such as Anaconda) installed

Notebooks and slides will be made available in GitHub:

https://github.com/DataForScience/DeepLearning

What you'll learn

Implement a subset of the functionality of Keras
Learn to work directly with state-of-the-art deep learning libraries in more advanced applications

Bruno Goncalves

Data For Science

Bruno Gonçalves is a chief data scientist at Data For Science, working at the intersection of data science and finance. Previously, he was a data science fellow at NYU’s Center for Data Science while on leave from a tenured faculty position at Aix-Marseille Université. Since completing his PhD in the physics of complex systems in 2008, he’s been pursuing the use of data science and machine learning to study human behavior. Using large datasets from Twitter, Wikipedia, web access logs, and Yahoo! Meme, he studied how we can observe both large scale and individual human behavior in an obtrusive and widespread manner. The main applications have been to the study of computational linguistics, information diffusion, behavioral change and epidemic spreading. In 2015, he was awarded the Complex Systems Society’s 2015 Junior Scientific Award for “outstanding contributions in complex systems science” and in 2018 was named a science fellow of the Institute for Scientific Interchange in Turin, Italy.