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PyBrain

PyBrain
PyBrain is a modular Machine Learning Library for Python. Its goal is to offer flexible, easy-to-use yet still powerful algorithms for Machine Learning Tasks and a variety of predefined environments to test and compare your algorithms. PyBrain is short for Python-Based Reinforcement Learning, Artificial Intelligence and Neural Network Library. In fact, we came up with the name first and later reverse-engineered this quite descriptive "Backronym". How is PyBrain different? While there are a few machine learning libraries out there, PyBrain aims to be a very easy-to-use modular library that can be used by entry-level students but still offers the flexibility and algorithms for state-of-the-art research.

http://pybrain.org/

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Irregular verbs again I have already published several posts on irregular verbs: and . However, a week ago a student of mine contacted me and asked me if I could create a way for him to learn the irregular verbs. He spends a lot of time driving so he asked me to prepare something to listen to in his car. So I did. In this post there are 33 irregular verbs presented in an associative matrix, in mp3 for listening, in mp3 for learning and two games for practising them. machine learning in Python — scikit-learn 0.13 documentation "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]."

Gusto - gusto - What is Gusto? - Gusto is a set of APIs for building intelligent web applications with semantic similarity, collaborative filtering, recommandation, etc. Gusto is a set of APIs for building intelligent web 2.0 applications more easily. Rather than writing your own logic from scratch, you can include Gusto's modules for semantic similarity measures, clustering, collaborative and recommendation functionalities, etc. Gusto is at an early stage, it is aimed to contain of the following modules :

Introduction to Natural Language Processing with Python In this talk, Jess Bowden introduces the area of NLP (Natural Language Processing) and a basic introduction of its principles. She uses Python and some of its fundamental NLP packages, such as NLTK, to illustrate examples and topics, demonstrating how to get started with processing and analysing Natural Languages. She also looks at what NLP can be used for, a broad overview of the sub-topics, and how to get yourself started with a demo project. This talk was part of AsyncJS (May event). [00:00:08] Today, I’m going to be talking about natural language processing, specifically with Python, just a bit of an introduction.

Identifying emotions based on brain activity and machine-learning techniques The image shows the average positions of brain regions used to identify emotional states (credit: Karim S. Kassam et al./Carnegie Mellon University) Scientists at Carnegie Mellon University have identified which emotion a person is experiencing based on brain activity. Irregular Verbs — Exercise 2 Directions: In the exercise that follows, you will read sentences that contain blanks. These blanks require the appropriate forms of irregular verbs. To keep track of your answers, print the accompanying handout.

Resurgence in Neural Networks - tjake.blog If you’ve been paying attention, you’ll notice there has been a lot of news recently about neural networks and the brain. A few years ago the idea of virtual brains seemed so far from reality, especially for me, but in the past few years there has been a breakthrough that has turned neural networks from nifty little toys to actual useful things that keep getting better at doing tasks computers are traditionally very bad at. In this post I’ll cover some background on Neural networks and my experience with them. Then go over the recent discoveries I’ve learned about. New open-source Machine Learning Framework written in Java Machine Learning & Statistics Programming 331 314 618Share436Share2286Share2 I am happy to announce that the Datumbox Machine Learning Framework is now open sourced under GPL 3.0 and you can download its code from Github! What is this Framework? The Datumbox Machine Learning Framework is an open-source framework written in Java which enables the rapid development of Machine Learning models and Statistical applications. It is the code that currently powers up the Datumbox API.

spaCy Usage Documentation On this page, we'll be featuring demos, libraries and products from the spaCy community. Have you done something cool with spaCy? Let us know! Artificial Intelligence and Machine Learning A Gaussian Mixture Model Layer Jointly Optimized with Discriminative Features within A Deep Neural Network Architecture Ehsan Variani, Erik McDermott, Georg Heigold ICASSP, IEEE (2015) Adaptation algorithm and theory based on generalized discrepancy Corinna Cortes, Mehryar Mohri, Andrés Muñoz Medina Proceedings of the 21st ACM Conference on Knowledge Discovery and Data Mining (KDD 2015) Adding Third-Party Authentication to Open edX: A Case Study John Cox, Pavel Simakov Proceedings of the Second (2015) ACM Conference on Learning @ Scale, ACM, New York, NY, USA, pp. 277-280 An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections Yu Cheng, Felix X.

Irregular Verbs — Exercise 3 Directions: In the exercise that follows, you will read sentences that contain blanks. These blanks require the appropriate forms of irregular verbs. To keep track of your answers, print the accompanying handout. If you are unsure which choice to make, consult the rules. Disclaimer: All prizes in this exercise are cyber, which means they have no physical reality and cannot be collected for use in the material world. UFLDL Tutorial - Ufldl From Ufldl Description: This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep Learning. By working through it, you will also get to implement several feature learning/deep learning algorithms, get to see them work for yourself, and learn how to apply/adapt these ideas to new problems.

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