Deep Learning

Getting Started with Recurrent Neural Networks (RNNs) and Convolutional Neural Networks

This page is intended for people who have some background in Machine Learning and want to learn more about Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) and apply them to their datasets.

LKS-CHART projects that apply neural nets include An Early Warning System for General Internal Medicine and Automated Analysis of Cervical Spine CT Scans in Trauma.  

Learning about RNNs:

 

People new to the field of RNNs might benefit more from the following:

 

Getting Started with RNNs: Warm-up

 

Getting Started with RNNs: Applying RNNs to your own dataset

Convolutional Neural Networks 

  • Stanford's CS231n is an up-to-date introduction to Convolutional Neural Networks, and is updated every year.

  • Goodfellow et al.'s Deep Learning provides a definitive introduction to deep learning, including Convolutional Neural Networks.

  • Michael Nielsen's Neural Networks and Deep Learning provides a gentler introduction to neural networks.

 

Programming Deep Neural Network Systems

  • Deep Learning with R by Francois Chollet with J.J Allaire is a great overview of deep learning with applications in R. Examples use the Keras library for R.

Machine Learning

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