Neural Networking - Back-Propagation

Overall, this is a helpful resource for those looking to learn more about neural networks. In summary, the conversation discusses a website that provides an introduction to neural networks and asks for feedback on its content. The website includes basic concepts and terminology related to neural networks and an example of implementing a neural network in Python. The suggestion is to add more information on different types of neural networks and designing and training techniques. Overall, the website is considered a helpful resource for those interested in learning about neural networks.
  • #1
zzmanzz
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Homework Statement




Hi, I have created a web-site which is supposed to walk someone with a minor or major in undegraduate mathematic through implementing a simple neural network. I would like some feed-back or comments on my work in order to determine whther the content thus far is sufficent for a basic introduction to neural networks. The web page is:

https://sites.google.com/site/informationnation100/neural-networking

There is not much content and if anyone can just glance over it I'd truly appreciate it.

Thank You
 
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  • #2
!Homework Equations N/A The Attempt at a Solution I think this is a great introduction to neural networks. It does a good job of introducing some of the basic concepts and terms related to neural networks, such as weights, neurons, and backpropagation. It also provides an example of how to implement a neural network in Python. The only thing I would suggest adding is some further explanation of the different types of neural networks (e.g., feed-forward, recurrent, etc.), as well as some more detailed information on how to design and train a neural network.
 

1. What is back-propagation in neural networking?

Back-propagation is a type of learning algorithm used in artificial neural networks to train the network by adjusting the weights of the connections between neurons. It involves the calculation of error at the output layer and then propagating this error back through the network to adjust the weights. This process is repeated multiple times until the network reaches a desired level of accuracy.

2. Why is back-propagation important in neural networking?

Back-propagation is important because it allows neural networks to learn and improve their performance over time. By adjusting the weights based on the calculated error, the network can make more accurate predictions and classifications. It also enables the network to handle complex and non-linear relationships between inputs and outputs.

3. How does back-propagation work?

Back-propagation works by using a gradient descent algorithm to minimize the error between the predicted output and the actual output. It involves calculating the gradient of the error with respect to each weight in the network and then adjusting the weights in the direction of the gradient. This process is repeated multiple times until the network reaches a minimum error.

4. What are the limitations of back-propagation?

One of the main limitations of back-propagation is the potential for overfitting, where the network becomes too specialized to the training data and does not generalize well to new data. It also requires a large amount of training data and may get stuck in local minima during the optimization process.

5. How can back-propagation be improved?

There are several techniques that have been developed to improve back-propagation, such as using different activation functions, introducing regularization to prevent overfitting, and using more advanced optimization algorithms such as Adam or RMSprop. Additionally, the use of deep learning architectures and techniques such as batch normalization can also improve the performance of back-propagation in neural networks.

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