Questions tagged [neural-networks]

Network structure inspired by simplified models of biological neurons (brain cells). Neural networks are trained to "learn" by supervised and unsupervised techniques, and can be used to solve optimization problems, approximation problems, classify patterns, and combinations thereof.

Filter by
Sorted by
Tagged with
1
vote
1answer
270 views

What are good counter-examples when training an apple classifier? [closed]

I am doing a project in order to recognize an apple. (I am using Emgucv with Visual Studio 2010 C#, if that's relevant). My project is a classification (is or is not an apple). I have 2000 images of ...
3
votes
2answers
247 views

Language Classification + AWS ML: what am I doing wrong?

I'm evaluating Amazon's machine learning platform, and thought that I would give it a "simple" classification problem first. As a disclaimer, I am quite new to machine learning (hence my interest in ...
2
votes
0answers
66 views

Using the random forest algorithm to predict vectors [duplicate]

I know this might sound like a newbie question, but bear with me. I have read a paper where researchers use a random forest to predict species distribution, but in their study, they only predict a ...
11
votes
1answer
213 views

Google DeepDream Elaborated

I've seen a few questions on this site about Deep Dream, however none of them seem to actually speak as to what DeepDream is doing, specifically. As far as I've gathered, they seem to have changed the ...
6
votes
1answer
5k views

Does there exist a data compression algorithm that uses a large dataset distributed with the encoder/decoder?

If my goal were to compress say 10,000 images and I could include a dictionary or some sort of common database that the compressed data for each image would reference, could I use a large dictionary ...
5
votes
1answer
258 views

Google Deep Dream has these understandings?

From both my own exploration of Google Deep Dream using Dreamify for IOS, and from Google Image results on the topic. I've come to 3 conclusions about the networks understanding of images that seem ...
4
votes
1answer
110 views

Are genetic algorithms an effective way to train neural networks?

It seems to me that genetic algorithms would be an ideal way to train neural networks so that they come to have the right weights, since they are especially good at escaping local minima, and ...
4
votes
3answers
275 views

How are Neural Networks made so general?

After reading this blog about Deep Neural Networks learning about selfies I'm struck by how generic the network in question is. In short: I'm thinking of trying to write something vaguely similar for ...
2
votes
2answers
334 views

Support Vector Machines as Neural Nets?

This is more of a conceptual question. I have learned about Neural Nets, and I have some clue as to how Support Vector Machines work. I read somewhere however that given the appropriate kernel (is ...
1
vote
2answers
5k views

What's the difference between a rule based system and an artificial neural network?

I'm currently doing some reading into AI and up to this point couldn't find a satisfying answer to this question: what's the difference between a rule based system and an artificial neural network? ...
3
votes
1answer
67 views

Training given pairs of similar values, not labels

I have pairs of "similar" values $(x_i, y_i)$ drawn from a space $x_i, y_i \in S$, and want to train a neural network $N$ such that $N(x_i)$ would be "close" to $N(y_i)$ for all $i$, yet, to make it ...
2
votes
1answer
345 views

How does a recurrent connection in a neural network work?

I am reading a very interesting paper on genetic algorithms which define neural networks. I am familiar with how a feedforward neural network operates, but then I came across this: Where node #4 ...
2
votes
1answer
890 views

NeuroEvolution: NEAT algorithm innovation numbers

I have been reading up on the NeuronEvolution of Augmented Topologies and there's this little thing that's been bothering me. While reading Kenneth Stanley's Paper on NEAT I came on this figure here: ...
0
votes
0answers
72 views

Where to start studying about HTM?

I am looking for references (pedagogic and beginner friendly!) to these two topics, hierarchical temporal memory algorithms applied to deep planning problems (multi-layer) neural networks trained ...
0
votes
0answers
59 views

Hessian in reinforcement learning

The Hessian of multi-layered network exhibits known behaviour at critical points as shown in [1]. The tools of random matrix theory allow [2] to deduce the asymptotic distribution of the eigenvalues ...