Confusions about Hopfield neural networks -- Questions about this solved example

In summary, the speaker is from Nepal and explains that their country's examination system asks for types of questions instead of code. They mention that some of the questions are difficult and there are not many examples in the textbooks they have access to. They also talk about their confusion while studying about hopfield networks and not being able to find proper resources for their exam. They mention annotations for their confusions but do not specify where they are.
  • #1
shivajikobardan
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Homework Statement
hopfield network solved example
Relevant Equations
idk as there are no resources to learn them
I am from Nepal and instead of asking us code, these are types of questions that are asked in our country examination system.

IDK what's their purpose. But some of them are badly hard as there are not many examples about it in textbook(In Nepal we can't get the textbook reference that the syllabus is made upon, we can of course get international writers textbook but our syllabus and exam paper is based on Indian author textbooks and we don't get that here in Nepal)

So I was studying about hopfield network and got badly confused. I have listed my confusions with annonations. It is not like I don't understand anything, I do understand the gist, but I am not very clear about each and every concepts.

And I didn't even find proper resources to study this topics in reference to our exam in internet as well.



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  • #2
shivajikobardan said:
I am from Nepal and instead of asking us code, these are types of questions that are asked in our country examination system.
What are - I can't see any questions?

shivajikobardan said:
So I was studying about hopfield network and got badly confused. I have listed my confusions with annonations.
Where are these annotations?
 
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Likes shivajikobardan

FAQ: Confusions about Hopfield neural networks -- Questions about this solved example

What is a Hopfield neural network?

A Hopfield neural network is a type of artificial neural network that is commonly used for pattern recognition and optimization problems. It is a form of recurrent neural network, meaning that it has feedback connections that allow it to store and retrieve information. It was first proposed by John Hopfield in 1982.

How does a Hopfield neural network work?

A Hopfield neural network consists of a set of interconnected processing units, or neurons, that are organized into layers. Each neuron receives input from other neurons and produces an output based on its activation function. The network is trained by adjusting the connection weights between neurons, allowing it to learn and store patterns. When presented with a partial or noisy input, the network can retrieve the closest stored pattern as its output.

What are some applications of Hopfield neural networks?

Hopfield neural networks have been used in a variety of applications, including image and speech recognition, optimization problems, and associative memory tasks. They have also been applied in fields such as biology, economics, and physics.

What are some common confusions about Hopfield neural networks?

One of the common confusions about Hopfield neural networks is the difference between a Hopfield network and a Hopfield model. A Hopfield network refers to the actual implementation of the network, while a Hopfield model refers to the mathematical model used to describe the network's behavior.

Can you provide an example of a solved problem using a Hopfield neural network?

One example of a solved problem using a Hopfield neural network is the traveling salesman problem, which involves finding the shortest route that visits a given set of cities and returns to the starting point. The network can be trained to learn the optimal route and then used to find a solution for new sets of cities. Another example is image restoration, where the network can be trained to reconstruct a damaged or distorted image based on previously learned patterns.

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