## Code
Introduction to TensorFlow and Neural NetworksHere I include Python notebooks to get started with Tensorflow, Neural Neworks (NNs), Convolutional NNs, Word Embeddings and Recurrent Neural Networks. This is a personal wrap-up of all the material provided by Google's Deep Learning course on Udacity, so all credit goes to them.
Data Sets (Notebooks 1 & 2) [notMNIST data base with 4000 training images, 8Mb] (Notebooks 1 & 2) [notMNIST data base with 100000 training images, 101Mb] (Notebooks 1 & 2) [notMNIST data base with 200000 training images, 127Mb] (Notebooks 3 & 4) [text dataset, 31Mb] (Notebooks 5 & 6) [small MFCC data set for character recognition, 1Mb]
Mini-Course on Inference and Learning in discrete Bayesian NetworksPython library to perform Belief Propagation inference over discrete BN code, defined by the user by means of tabular Conditional Probability Distributions (CPDs). Four python notebooks are included: two to describe how to use the library and run BP inference, and two to show how to learn the BNs CPDs with hidden observations using EM. Slides are also provided [code].
Expected graph evolution during peeling decoding of LDPC codes and SC-LDPC codes constructed from protographsThis Matlab-MEX software can be used to analyze finite-length ensembles constructed from protographs over the BEC. The user can specify an arbitrary base matrix for the LDPC code and a channel parameter. As a result, the script gives the expected evolution of the fraction of degree-one check nodes in the graph during peeling decoding [code]. |