Deep Learning

Deep Learning Research

Fog Detection using Deep Learning
Research activities of PhD Student Ernir Erlingsson under the umbrella of the DEEP-EST EU Project.

Interesting Deep Learning Links

http://www.fz-juelich.de/ias/jsc/EN/AboutUs/Organisation/FederatedSystemsandData/CrossSectionalTeamDeepLearning/_node.html
Page of the cross-sectional team deep learning of the Juelich Supercomputing Centre in Germany.

https://deeplearning4j.org/neuralnet-overview
Interesting introduction to Deep Neural Networks (Deep Learning) including use cases and choosing related networks.


Morris Riedel Deep Learning

Long-Short Term Memory (LSTM) Regularization Links

https://machinelearningmastery.com/use-weight-regularization-lstm-networks-time-series-forecasting/
Interesting short tutorial on how to use regularization with LSTM models that often overfit training data lowering predictive performance. It covers a Shampoo Sales dataset example, experimental test harness, bias weight and input weight regularization as well as recurrent weight regularization. The tutorial uses Python as well as Keras on top of Tensorflow or Theano.

https://keras.io/regularizers/
Listings of supported regularization approaches in Keras on top of Tensorflow or Theano.

YouTube Videos

MIT 6.S191: Introduction to Deep Learning

Playlist (all lectures)

  1. Alexander Amini: MIT 6.S191 – Lecture 1: Introduction to Deep Learning, January 2018
  2. Harini Suresh: MIT 6.S191 – Lecture 2: Sequence Modeling with Neural Networks, January 2018
  3. Ava Soleimany: MIT 6.S191 – Lecture 3: Deep Learning for Computer Vision, January 2018