aashish.v said:The function is J(n-1)+J(n+1)=2nJ(n)
I need to prove that it diverges in forward direction but converges in backward direction.
I am unable to find any method, kindly suggest.
Ray Vickson said:You need to show your work.
RGV
The convergence problem for forward and backward propagation refers to the issue of the neural network not learning or improving its accuracy over time. This can happen due to various reasons such as incorrect network architecture, inappropriate learning rate, or insufficient training data.
The convergence problem can significantly impact the performance of a neural network as it prevents the network from learning and improving its accuracy. This can result in inaccurate predictions and poor performance on the given task.
Some common causes of the convergence problem include using an incorrect network architecture, choosing an inappropriate learning rate, not normalizing the input data, and having insufficient training data. Other factors such as vanishing or exploding gradients can also contribute to this problem.
The convergence problem can be solved by carefully selecting the network architecture and tuning the learning rate. It is also essential to normalize the input data and ensure that there is enough training data to learn from. Regularization techniques such as dropout and early stopping can also help prevent overfitting and improve convergence.
While the convergence problem can be minimized through careful tuning and regularization techniques, it cannot be completely eliminated. Neural networks are complex models, and there can always be some data or scenarios that the network struggles to learn from. It is important to continuously monitor and adjust the model to improve its performance.