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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.

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ForArtificialIntelligence This page attempts to collect information and links pertaining to the practice of AI and Machine Learning in python. GraphLab Create - An end-to-end Machine Learning platform with a Python front-end and C++ core. It allows you to do data engineering, build ML models, and deploy them. Brian/StimulusArrayGroup - NeuralEnsemble Cookbook - Trac This code is for creating a group of neurons which fire with a given 2D stimulus at a given rate. You initialise it with arguments: stimulus, which should be a 2D array with values between 0 and 1. rate, which should be a firing rate in Hz. onset, the time that the stimulus should become active. duration, the length of time for which the stimulus should be active. The point in the stimulus array at position (y,x) will correspond to the neuron with index i=y*width+x. This neuron will fire Poisson spikes at rate*stimulus[y,x] Hz.

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. Package Index : biblio.webquery 0.4.3b Extracting bibliographic information from web services This package presents a number of methods for querying webservices for bibliographic information, and includes two scripts for querying and renaming files by ISBN. biblio.webquery can be installed in a number of ways. setuptools is preferred, but a manual installation will suffice. Via setuptools / easy_install

Octave GNU Octave is a high-level interpreted language, primarily intended for numerical computations. It provides capabilities for the numerical solution of linear and nonlinear problems, and for performing other numerical experiments. It also provides extensive graphics capabilities for data visualization and manipulation. Octave is normally used through its interactive command line interface, but it can also be used to write non-interactive programs. The Octave language is quite similar to Matlab so that most programs are easily portable. Octave is distributed under the terms of the GNU General Public License.

Introducing public beta of Datalore – web application for machine learning Last Monday, February 12, we launched a public beta of Datalore – an intelligent web application for data analysis and visualization in Python. Today, machine learning is at the heart of many commercial applications and research projects. By introducing Datalore, we’re extending the JetBrains product family to the machine learning-specific environment in Python. We’re launching this tool inspired by the JetBrains vision – to make development as enjoyable and productive as possible for everyone. Datalore aims to turn working with data into a delightful experience with helpers such as smart coding assistance, incremental computations, and built-in tools for machine learning.

PyNN - Trac PyNN (pronounced 'pine') is a is a simulator-independent language for building neuronal network models. In other words, you can write the code for a model once, using the PyNN API and the Python programming language, and then run it without modification on any simulator that PyNN supports (currently NEURON , NEST , PCSIM and Brian ). The PyNN API aims to support modelling at a high-level of abstraction (populations of neurons, layers, columns and the connections between them) while still allowing access to the details of individual neurons and synapses when required. 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.

EPD - Frequently Asked Questions (FAQ) Q 10. Can I redistribute Canopy Express? Yes, there are a couple different ways you can redistribute Canopy Express or parts of Canopy Express. ActivePerl ActivePerl is the leading commercial-grade distribution of the open source Perl scripting language. Download ActivePerl Community Edition free binaries for your development projects and internal deployments. By downloading ActivePerl Community Edition's Perl binaries, you agree to comply with the terms of use of the ActiveState Community License. Building deep learning neural networks using TensorFlow layers Deep learning has proven its effectiveness in many fields, such as computer vision, natural language processing (NLP), text translation, or speech to text. It takes its name from the high number of layers used to build the neural network performing machine learning tasks. There are several types of layers as well as overall network architectures, but the general rule holds that the deeper the network is, the more complexity it can grasp. This article will explain fundamental concepts of neural network layers and walk through the process of creating several types using TensorFlow. TensorFlow is the platform that contributed to making artificial intelligence (AI) available to the broader public.

Neural Modeling with Python (Part 3) So far we've looked at how to simulate a simple LIF model neuron and a complex Hodgkin-Huxley model neuron. The LIF neuron is computationally simple but physiologically implausible, while Hodgkin-Huxley gives us a very good representation of actual neural dynamics but is parameter-heavy and computationally expensive. An intriguing compromise between the two exists -- one that can generate a wide variety of observed neural spiking behavior while doing so with limited computational demand. It is called the quadratic integrate-and-fire model neuron, or simply Izhikevich neuron. Izhikevich Model Neuron

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