A Neural Network in 11 lines of Python (Part 1) - i am trask
A bare bones neural network implementation to describe the inner workings of backpropagation. Posted by iamtrask on July 12, 2015 Summary: I learn best with toy code that I can play with. This tutorial teaches backpropagation via a very simple toy example, a short python implementation. Edit: Some folks have asked about a followup article, and I'm planning to write one. I'll tweet it out when it's
Self Driving RC Car
Objective Modify a RC car to handle three tasks: self-driving on the track, stop sign and traffic light detection, and front collision avoidance. System Design The system consists of three subsystems: input unit (camera, ultrasonic sensor), processing unit (computer) and RC car control unit. Input Unit A Raspberry Pi board (model B+), attached with a pi camera module and an HC-SR04 ultrasonic sens
Password recovery — NeuPy
Password recovery Contents Password recovery Data transformation Saving password into the network Recovering password from the network Test it using Monte Carlo Possible problems Summary Download script In this article we are going to build a simple neural network that will recover password from a broken one. If you aren’t familiar with a Discrete Hopfield Network algorithm, you can read this arti
How to implement a neural network: Part 1 | Peter's Notes
This page is the first part of this introduction on how to implement a neural network from scratch with Python and NumPy. This first part will illustrate the concept of gradient descent illustrated on a very simple linear regression model. The linear regression model will be approached as a minimal regression neural network. The model will be optimized using gradient descent, for which the gradien
Neural Networks in Python
Artificial Neural Networks are a mathematical model, inspired by the brain, that is often used in machine learning. It was initially proposed in the '40s and there was some interest initially, but it waned soon due to the inefficient training algorithms used and the lack of computing power. More recently however they have started to be used again, especially since the introduction of au
Basic [1 hidden layer] neural network on Python | Daniel Rodriguez
Is really difficult be part of the data science / machine learning community and have not heard about Deep Neural Networks, everybody is talking about them. Is even harder for a person like me without a PhD and without a deep computer science or mathematics education to learn about them, because 1. Machine learning uses a quite heavy math and 2. There are no Neural Networks on sklearn. Libraries l
Neural network example using Pylearn2 | Gustav's blog
I was recently looking into using a neural network for a project so I started looking into some of the available Python libraries. The one I ended up using was Pylearn2 which is a fast and powerful library for machine learning that is mainly built upon Theano. Pylearn2 is under development and is still a bit rough around the edges and the documentation is limited and in some instances not correct.
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CS231n Convolutional Neural Networks for Visual Recognition
GitHub - siddharth-agrawal/Neural-Tensor-Network
A not so basic neural network on python | Daniel Rodriguez