Meet Musio

Theano: Theano Python Language Library

Goal In this ongoing summary we give a first introduction to the theano library, its basic functionality and usage in th field neural networks. Motivation The need for such a library is based on easily handling tensorial objects, like multi-array input data and weights in neural networks. Of particular interest are symbolic operations that can perform differentiation as need for the backpropagation algorithm in order to update the models according to the seen training data. A core functionality of libraries nowadays is to make use of GPUs in order to efficiently distribute the huge amount of computing operations. Ingredients symbolic operations, numpy, python Steps The theano library is an open source project lead by a machine learning group associated with […]

Answering Tasks for AI

Goal The aim of this short summary is to provide an overview of the tasks an AI system is, should be and will be capable to execute in the near future. In particular we focus on classifying tasks with respect to NLP. Motivation When testing existing algorithms it is of great importance to have a clear interpretation of the results. This can be achieved by setting up specific learning environments in such a way that the complexity of the task is always under control. More generally, the state-of-the-art problems determine next development steps for Musio. Ingredients common sense, coreference, compound, conjunction, deduction, induction, argument relation, counting, negation, indefinite knowledge, time reasoning, positional and size reasoning, path finding, motivational reasoning. Steps […]

Deep learning

Deep Learning and Musio

Deep learning is a branch of Machine learning and is being actively explored in the recent years. Thanks to deep learning algorithms, it enables Musio to learn like us humans and let him do what he is capable of doing. Deep learning methods are better than the traditional machine learning and tech companies are always improving this area of technological advancements. The traditional machine learning techniques works in this way. If a program needed to be coded to recognise hand writing for instance, it’s quite a complex application to develop. The developer would need to devise rules on how characters are written, like ‘O’ is a rounded closed circle, but if someone writes it open on top, the application ‘thinks’ it’s a ‘U’ and here is […]

A Basic Tutorial to Logistic Regression

For more info  : log presenter : Jay The seminar covered the basics of logistic regression, which is the simplest method for classification. After going through an numeric example and the principle of maximum likelihood, an overview of logistic regression was given in relationship with other algorithms.The following was shown: 1. The equivalence between the Maximum Entropy algorithm and logistic regression 2. The equivalence between Naive bayes and logistic regression 3. How Conditional Random Fields are basically logistic regression with selective mapping of feature functions Conclusively, it was shown that logistic regression is a simple algorithm which works well when classifying linearly separable data with a reasonable running time.