Keeping up with the Deep-Learning Curve via Keras
Keeping up with the Deep-Learning Curve via Keras


Certainly, some of the most exciting research going on right now is in the area Deep-Learning. But how do we get started with hands-on practice and how do we gain a basic understanding of what is going on within all of those deep learning layers?

This lesson will help the beginner-level deep-learner navigate this new landscape. I will explain both the design theory and the Keras implementation of some of today’s most widely used deep-learning algorithms including convolutional neural nets and recurrent neural nets.

I will also discuss some of my own recent explorations via Keras including a spin-off of Style Transfer.


Julia Lintern is a senior data scientist at Metis, where she co-teaches the data science bootcamp, develops curricula, and focuses on other special projects. Previously, Julia worked as a data scientist at JetBlue, where she used quantitative analysis and machine learning methods to provide continuous assessment of the aircraft fleet. Julia began her career as a structures engineer designing repairs for damaged aircraft. Julia holds an MA in applied math from Hunter College, where she focused on visualizations of various numerical methods including collocation and finite element methods and discovered a deep appreciation for the combination of mathematics and visualizations, leading her to data science as a natural extension of these ideas. She continues to collaborate on various projects; including her current work with the NYTimes data science team. During certain seasons of her career, she has also worked on creative side projects such as Lia Lintern, her own fashion label.

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