This week is the last week of the Stanford Machine Learning course (ml-class.org), so here is my basic review of the course. Overall, the quality is quite good. The lectures are easily the most valuable part, as Andrew Ng is very organized and provides insightful explanations over the approaches and algorithms. The homework problems use Octave, a mostly clone version of Matlab (including down to language spec), so it’s pretty easy to pick up for most …

## Biological Neural Networks definition

The definition of Bioinformatics changes for me, with every new research paper I read, every new bioinformatics definition I go through and every new lecture I attend related to Bioinformatics. Bioinformatics is a research area which is as endless as a sea, where there are no boundries, therefore no single definition can completely explain it. So here I am with my story of bioinformatics definitions… After completion of my A’levels degree, when I …

## Best language Neural Networks

My question is which is the best language that can be used to implement the components in the Computer (Neural Network. ANNs can be written in any language. I'm not looking for a Neural Networks library, since I'm creating new kinds of networks. For that I need a good "dataflow" language.

## Neural Networks Backpropagation tutorial

A Neural Network (or artificial neural network) is a collection of interconnected processing elements or nodes. The nodes are termed simulated neurons as they attempt to imitate the functions of biological neurons. The nodes are connected together via links. We can compare this with axon-synapse-dendrite connections in the human brain. Initially, a weight is assigned at random to each link in order to determine the strength of one node’s influence …

## Neural Network Graphical model

The following excerpt from “A Brief Introduction to Graphical Models and Bayesian Networks” by Kevin Murphy. Books In reverse chronological order. Daphne Koller and Nir Friedman, “Probabilistic graphical models: principles and techniques”, MIT Press 2009 Adnan Darwiche, “Modeling and reasoning with Bayesian networks”, Cambridge 2009 F. V. Jensen. “Bayesian Networks and Decision Graphs”. Springer. 2001. Probably the best introductory book available …

## Neural Networks Lecture

Introduction to neural networks video lectures by prof.H Sebastian Seung, of MIT for the year 2005 are available for free. Videos can viewed directly on MIT. The 'lecture' meeting will always be a discussion of the material of that day. Video 1b: What are neural networks? Video 1c: Some simple models of. Back to Engineering Video Lecture Course Page · Lec-1 Introduction to Artificial Neural Networks; Electronics Engineering; …

## RBF Neural Networks Applications

With the rapid development of national economy and integrative strength, the highway and bridge construction in our country also increase very fast.Pre-stressed concrete continuous girder bridges have many advantages, such as easy construction, small deformation, reasonable cost and strong spanning capacity, and that’s why it’s widely used.During the construction of the pre-stressed concrete continuous bridges by using the cast-in-place cantilever …

## NeuronDotNet - Neural Networks in c

I will be looking into using AI to make money in Fx. I will most likely be using neural networks. To that end after I finish playing round in the demo version on Alpari and having traded a bit I will embark on my holy grail journey. Some of the websites I will be using include : C# neural networks engine : a fella who is developing a neural network program to make money in horse betting. I will of coz do more research on this topic but most people …