## Machine Learning Courses
Introduction 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! Notebook 1: How to train a logistic-regressor and a 2-layer NN with L2-norm regularization using TensorFlow.
Notebook 2: Convolutional NNs and Dropout Regularization
Notebook 3: Word Embeddings and the wor2vec model
Notebook 4: Recurrent NNs and sequential character prediction
Notebook 5: Recurrent NNs and sequential character prediction from MCC features with Connectionist Temporal Classification
Notebook 6: Bi-directional LSTM RNN and sequential character prediction from MCC features with Connectionist Temporal Classification
Notebook 7: Amortized Variational Inference with Neural Networks and Variational Autoencoders
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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: Introduction to Python Notebooks and Numpy Introduction to Pandas Linear regression with a single variable Learning curves, regularization, and cross validation Numerical optimization with gradient descend Nonparametric regression Introduction to binary classification and logistic regression The kernel trick in logistic regression Support Vector Machines Multiclass classificaiton Unsupervised Learning. Clustering with K-means Clustering with Gaussian Mixture Models and the EM algorithm Dimensionality Reduction with PCA. Probabilistic PCA. Latent-Dirichelet Allocation. Finding similar documents in a corpus of documents. Introduction to Neural Networks & Tensorflow Building an image classifier with Convolutional Neural Networks Word Embeddings Recurrent Neural Networks for text prediction
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
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.
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