What is ResNet and where will you use it? Is it effective?
Among the various neural networks used for computer vision, ResNet (Residual Neural Network) is one of the most popular. It allows us to train extremely deep neural networks, which is the main reason why it is so widely used and popular. Before the invention of this network, it was almost impossible to train extremely deep neural networks. To understand why, we need to look at the vanishing gradient problem, which is essentially the problem that arises when the gradient is propagated back to all layers. When a large number of multiplications are performed, the size of the network continues to decrease until it becomes extremely small and thus the network begins to malfunction. ResNet helps to solve the leak gradient problem.
The efficiency of this network depends heavily on the concept of the connection hop. Bypassing connections is a method that allows a short path through which the gradient can flow, which really helps with the end-of-band problem. In general, an omitted connection allows us to skip training a few layers. Hopping connections are also known as identity shortcut connections because they allow us to directly compute an identity function by relying only on these connections, without having to consider the entire network.
Bypassing these layers makes ResNet an extremely efficient network.
Dropout is an essential requirement in some neural networks. Why is it necessary? Overfitting is probably one of the biggest problems when it comes to neural networks. This happens when a complex model is used for a very small data set. This obviously leads to very poor performance.
To combat overlearning, one of the most helpful methods is procrastination. Dropout uses different architectures in parallel to train neural networks. Some classes that are dropped randomly during training are, in fact, called dropout classes.
When a stagnation occurs, some units are forced to correct errors caused by other units. In general, abort is performed on any layer except the output layer. Dropout use cases can happen for all types of networks, including convolutional neural networks, short-term long-term memory (LSTM) networks, and more.
Note that both hidden and visible layers can be deleted. At the end of the elimination process, a reduction network, with discarded input and output edges for each discarded node, is created.
The overall probability of a node being deleted is 0.5. Indeed, since learning is not done on all nodes, over-learning is reduced. It also helps the model learn more general features, which can then be used to learn new data faster and better.
Dropping usually yields better performance on large networks. Dropout often works best with high learning rates but with an element of decline. What is a Sobel filter?
How would you implement it in Python?
The Sobel filter performs a two-dimensional spatial gradient measurement on a given image, then emphasizes regions of high spatial frequency. In practice, this means finding edges.
In most cases, the Sobel filter is used to find the approximate absolute magnitude of the slope for each point in the grayscale image. The operator consists of a pair of 3 × 3 convolution kernels. One of these kernels is rotated 90 degrees.
These kernels respond to horizontal or vertical edges relative to the pixel grid, one for each direction. One point to note is that these kernels can be applied separately or can be combined to find the absolute magnitude of the gradient at each point.
