Introduction to Understanding Deep Learning
The goal of this non-intimidating session is to make deep learning more approachable. We will unravel some of the complexities. There will be no coding. We will differentiate between machine learning and the sub-field of deep learning, and pursue a more intuitive understanding of how deep learning is implemented. Primarily, we will explore neural networks and their most common architectures, focusing on the deep neural networks that are most effectively used with visual data such as image recognition (e.g., Convolutional Neural Networks), and those most amenable to sequential data like natural language processing (e.g., Recurrent Neural Networks). The aim is for attendees to become conversant with deep learning and to acquire a clearer understanding of the concept as well as its application.
Antony Ross is a consultant specializing in data science and machine learning applied to sports performance. He has worked closely with USC and UCLA and is presently involved with the Recurse Center in New York researching deep learning and voice recognition.
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