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International Journal of Advanced Computer Science and Applications • Vol 13 • No 11
Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks
2022
Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimizat…
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Backpropagation

Optimization algorithm for artificial neural networks

As a machine-learning algorithm, backpropagation performs a backward pass to adjust a neural network model's parameters, aiming to minimize the mean squared error (MSE). In a multi-layered network, backpropagation uses the following steps:

  1. Propagate training data through the model from input to predicted output by computing the successive hidden layers' outputs and finally the final layer's output (the feedforward step).
  2. Adjust the model weights to reduce the error relative to the weights.
    1. The error is typically the squared difference between prediction and target.
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International Journal of Advanced Computer Science and Applications • Vol 13 • No 11
Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks
2022
Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimization is difficult in the CNN is the existence of a huge optimization space comprising a large number of hyper-parameters such as the number of layers, number of neurons, number of kernels, stride, padding, rows or columns truncation, parameters of the backpropa…
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Backpropagation
International Journal of Advanced Computer Science and Applications • Vol 13 • No 11
Constraints on Hyper-parameters in Deep Learning Convolutional Neural Networks
2022
Convolutional Neural Network (CNN), a type of Deep Learning, has a very large number of hyper-meters in contrast to the Artificial Neural Network (ANN) which makes the task of CNN training more demanding. The reason why the task of tuning parameters optimizat…
Article
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Narrative
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