Machine Learning CoursesIntroduction to TensorFlow and Neural NetworksPython notebooks to get started with Tensorflow, Neural Neworks (NNs), Convolutional NNs, Word Embeddings and Recurrent Neural Networks. Most of the material is a personal wrap-up of all the material provided by Google's Deep Learning course on Udacity,so all credit goes to them. Additionally, I added one more notebook to practice with the CTC loss function in temporal models and a another two about Variational Inference with Neural Networks and Variational Autoencoders. Python 3.X required!
Note: since this is an introductory course, most of the steps to define the computation graph in TensorFlow are manually implemented, e.g. I do not make use of predefined tf.layers. We have moved all files to this public Github repository. Introduction to Data Science and Machine LearningTeaching material for an introductory course on Data Science and Machine Learning. I have structured the course with 18 lessons organized as follows:
Important note: this course is still under construction! We expect to have all lessons available by June 2018. Until then, a new lesson will weekly be uploaded. Currently, sessions 1 to 8 are available. For every lesson, I provide a self-contained notebook with both models descriptions and theoretical description (using integrated LaTeX Markdown blocks) and practical examples. We have moved all files to this public Github repository. 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. Python 3.X required! We have moved all files to this public Github repository. |