I've Googled, StackOverflowed, everything, and I cannot seem to find a tutorial I can understand. I understand the concept of genetic algorithms, and how to implement them, (Though I haven't tried) but I cannot grasp the concept of neural networks. I know vaguely how they work... And that's about it. Could someone direct me to a tutor...
I want to do a riddle AI chatbot for my AI class. So i figgured the input to the chatbot would be : Something like : "It is blue, and it is up, but it is not the ceiling" Translation : <Object X> <blue> <up> <!ceiling> </Object X> (Answer : sky?) So Input is a set of characteristics (existing \ not existing in the ob...
Can anyone all the different techniques used in face detection? Techhniques like neural networks, support vector machines, eigenfaces, etc. What others are there? Thanks. ...
I'm trying to determine how to transform my "meaningful input" into data for an Artificial Neural Network and how to turn the output into "meaningful output". The way I can always see of doing it is by convering everything to categories with binary values. For example, rather than outputting age, having a 0-1 for <10, a 0-1 for 10 - 19...
Do you have some advices or reading how to engineer features for a machine learning task? Good input features are important even for a neural network. The chosen features will affect the needed number of hidden neurons and the needed number of training examples. The following is an example problem, but I'm interested in feature engineer...
edit: A more pointed question: What is the derivative of softmax to be used in my gradient descent? ============== This is more or less a research project for a course, and my understanding of NN is very/fairly limited, so please be patient :) ============== I am currently in the process of building a neural network that attempts to...
There's various activation functions: sigmoid, tanh, etc. And there's also a few initializer functions: Nguyen and Widrow, random, normalized, constant, zero, etc. So do these have much effect on the outcome of a neural network specialising in face detection? Right now I'm using the Tanh activation function and just randomising all th...
I've been looking at face detection lately, and a lot of the literature states their outputs have a range. How is this possible? I've created my own network and it only seems to be outputting either -1 or 1. Is this because I'm using the Tanh activation function? I want the values to output from, say, 0 to 1 in a range, rather than a ...
I've witnessed a debate on this topic, I wanted to know your opinion ? From a programmers perspective, can a we consider a neural network a more advanced "data structure" ? Is a neural network actually more than a data-structure ? ...
I intend to use Reinforcement learning in my project but I do not know much how to implement it.. So I am looking for a library with different RL algorithms that I can use in my C# project.. Thanks Please Note: I found NeuronDotNet library for neural networks, I am now looking for RL library.. EDIT: Or a Dot NET library ...
I am working on a project with RL & NN I need to determine the action vector structure which will be fed to a neural network.. I have 3 different actions (A & B & Nothing) each with different powers (e.g A100 A50 B100 B50) I wonder what is the best way to feed these actions to a NN in order to yield best results? 1- feed A/B to inpu...
I'm trying to create a single layer perceptron. If I use the nnet function to create a n-0-1 network (n being the number of inputs), is that equivalent to a perceptron? ...
I am trying to use Multi-Layer NN to implement probability function in Partially Observable Markov Process.. I thought inputs to the NN would be: current state, selected action, result state; The output is a probability in [0,1] (prob. that performing selected action on current state will lead to result state) In training, I fed the in...
Does increasing the number of test cases training data in case of Precision Neural Networks may led to problems (like over-fitting for example)..? Does it always good to increase test cases training data number? Will that always lead to conversion ? If no, what are these cases.. an example would be better.. Thanks, ...
Hi All. I am looking for some kind of intelligent (I was thinking AI or Neural network) library that I can feed a list of historical data and this will predict the next sequence of outputs. As an example I would like to feed the library the following figures 1,2,3,4,5 and based on this, it should predict the next sequence is 6,7,8,9,1...
I´ve been searching for it and I found Encog and Neuroph but I don´t know if any about them... I've to do a final project and I need a litle feedback from humans, sometimes google is not enough XD ...
Is it a good practice to use sigmoid or tanh output layers in Neural networks directly to estimate probabilities? i.e the probability of given input to occur is the output of sigmoid function in the NN EDIT I wanted to use neural network to learn and predict the probability of a given input to occur.. You may consider the input as Stat...
I've read in Wikipedia that neural-network functions defined on a field of arbitrary real/rational numbers (along with algorithmic schemas, and the speculative `transrecursive' models) have more computational power than the computers we use today. Of course it was a page of russian wikipedia (ru.wikipedia.org) and that may be not properl...
There is a question I am stuck on using the following formula for the unipolar transfer function: f(net)= 1 __________ -net 1 + e The example has the following: out = 1 ____________ = 0.977 -3.75 1 + e How do we arrive at 0.977? What is e? ...
Our brain consists of billions of neurons which basically work with all the incoming data from our senses, handle our consciousness, emotions and creativity as well as our hormone system, etc. So I'm completely new to this topic but doesn't each neuron have a fixed function? E.g.: If a signal of strength x enters, if the last signal was...