How to improve the performance of convolutional neural network?
Improving Performance of Convolutional Neural Network! Convolutional Neural Network — a pillar algorithm of deep learning — has been one of the most influential innovations in the field of computer vision. They have performed a lot better than traditional computer vision algorithms.
How to optimize hyperparameter tuning in neural networks?
A step-by-step Jupyter notebook walkthrough on hyperparameter optimization. This is the fourth article in my series on fully connected (vanilla) neural networks.
Which is the best way to train a neural network?
This involves:- Standardizing/Normalizing the inputs can make training a lot faster and reduce the chances of getting stuck in local optima hence it is highly recommended before inputting the data into the network to train
What are the parameters of a neural network?
Hyper-parameter is a configurable value which is set before the learning process begins. These hyper-parameter values dictate the behavior of the training algorithm and how it learns the parameters from the data.
How is softmax used in convolutional neural network?
After passing through the fully connected layers, the final layer uses the softmax activation function (instead of ReLU) which is used to get probabilities of the input being in a particular class ( classification ). And so finally, we have the probabilities of the object in the image belonging to the different classes!!
How to build convolutional neural network for multi-class classification?
Building a convolutional neural network for multi-class classification in images Some of the computer vision problems which we will be solving in this article are: One major problem with computer vision problems is that the input data can get really big. Suppose an image is of the size 68 X 68 X 3. The input feature dimension then becomes 12,288.
How is a Sobel filter used in a convolutional neural network?
The Sobel filter puts a little bit more weight on the central pixels. Instead of using these filters, we can create our own as well and treat them as a parameter which the model will learn using backpropagation. We have seen that convolving an input of 6 X 6 dimension with a 3 X 3 filter results in 4 X 4 output.