Unraveling the Mystery of "Neural Networks"

In summary, Neural networks are used to study and model networks with feedback. They can be trained to do things without actually understanding how they do it.
  • #1
Brian_C
250
0
Can anyone explain to me what this "neural network" nonsense is all about? Everyone and their mother is doing research in this area, but nobody seems to know what "neural networks" are or why they would be useful. It sounds like pure hype to me.
 
Physics news on Phys.org
  • #2
Brian_C said:
Can anyone explain to me what this "neural network" nonsense is all about?

c&p from my psychology paper:
In its most basic form, a neural net is a a set of units connected by statistically weighted links. (Russel & Norvig, 1995, p. 567). The units are mathematical stand ins for neurons-they do all the actual processing based on some set of inputs (including a current activation level) and they give back some output (including a new activation level). The weights on the links determine how important that specific link (signal) will be in the neurons computation, and these signals are then passed on to the next unit(s) in the chain, continuing until the signal has passed through whatever hierarchy it was supposed to. (Russel & Norvig, 1995, chap. 19) The weights are adjusted through a series of trials so that each node can learn which signals it needs to pay attention to and which ones it should filter out, making neural networks very suited to studying attention tasks, wherein every neuron has to decide what computational weight it wants to attach to each activation potential traveling through it.

Basically, people like neural nets 'cause they're good for studying/modeling networks with feedback.
 
Last edited:
  • #3
story645 said:
Basically, people like neural nets 'cause they're good for studying/modeling networks with feedback.

you can also train them to do things without actually understanding how they do it.

i wonder if Toyota uses them to control any of their automotive systems ? :uhh:
 
  • #4
story645 said:
c&p from my psychology paper:
In its most basic form, a neural net is a a set of units connected by statistically weighted links. (Russel & Norvig, 1995, p. 567). The units are mathematical stand ins for neurons-they do all the actual processing based on some set of inputs (including a current activation level) and they give back some output (including a new activation level). The weights on the links determine how important that specific link (signal) will be in the neurons computation, and these signals are then passed on to the next unit(s) in the chain, continuing until the signal has passed through whatever hierarchy it was supposed to. (Russel & Norvig, 1995, chap. 19) The weights are adjusted through a series of trials so that each node can learn which signals it needs to pay attention to and which ones it should filter out, making neural networks very suited to studying attention tasks, wherein every neuron has to decide what computational weight it wants to attach to each activation potential traveling through it.

Basically, people like neural nets 'cause they're good for studying/modeling networks with feedback.

A guy I met here in San Diego described them as computer programs that can "learn" in some way, shape or form. He, himself, was teaching a neural net to play poker, or, perhaps, allowing it to learn how to play poker would be a better description because all he was doing was waiting while the program ran for weeks by itself.
 
  • #5
zoobyshoe said:
A guy I met here in San Diego described them as computer programs that can "learn" in some way, shape or form.
That's because they're feedback functions, so outcomes are used to reweight paths until the weights are settled such that a "correct" output is reached.
 

1. What are neural networks?

Neural networks are a type of artificial intelligence that uses interconnected nodes to process information and learn from it. They are modeled after the way the human brain works and are used for tasks such as image recognition, speech recognition, and natural language processing.

2. How do neural networks work?

Neural networks work by taking in a set of input data, processing it through multiple layers of interconnected nodes, and producing an output. Each node performs a simple mathematical operation on the input data and passes the result to the next layer. Through a process called backpropagation, the network adjusts the connections between nodes to improve its performance on a specific task.

3. What is the difference between deep learning and neural networks?

Deep learning is a subset of neural networks that uses multiple layers to process information and learn from it. While all deep learning models are neural networks, not all neural networks are deep learning models. Deep learning models are able to handle more complex tasks and often outperform traditional neural networks.

4. What are the applications of neural networks?

Neural networks have a wide range of applications in various industries, including healthcare, finance, marketing, and robotics. They are used for tasks such as predicting stock prices, diagnosing diseases, and autonomous driving. They are also used in everyday technology, such as virtual assistants and social media algorithms.

5. What are the limitations of neural networks?

While neural networks have shown great success in many tasks, they also have some limitations. They require a large amount of training data to perform well, and their decision-making process is often seen as a "black box," meaning it can be difficult to interpret and understand how they arrived at a particular result. They also require significant computational power and can be prone to overfitting, where the network performs well on training data but fails to generalize to new data.

Similar threads

  • MATLAB, Maple, Mathematica, LaTeX
Replies
1
Views
1K
Replies
26
Views
3K
  • Programming and Computer Science
Replies
18
Views
1K
  • Programming and Computer Science
Replies
4
Views
497
  • Science and Math Textbooks
Replies
2
Views
888
  • Computing and Technology
Replies
4
Views
1K
  • General Discussion
Replies
5
Views
886
Replies
6
Views
684
  • Programming and Computer Science
Replies
1
Views
868
  • Programming and Computer Science
Replies
4
Views
1K
Back
Top