8 basic terms related to Deep Learning
Convolution:
Convolution is a mathematical operation on two functions (f and g) that produces a third function (f*g) expressing how the shape of one is modified by the other. The term convolution refers to both the result function and to the process of computing it. It is defined as the integral of the product of the two functions after one is reversed and shifted. And the integral is evaluated for all values of shift, producing the convolution function.
Hyperparameter:
Hyperparameter is a parameter whose value is used to control the learning process. It can be classified as model hyperparameters, that cannot be inferred while fitting the machine to the training set because they refer to the model selection task, or algorithm hyperparameters, that in principle have no influence on the performance of the model but affect the speed and quality of the learning process.
Stride:
Stride is the number of pixels shifted over the input matrix. When the stride is 1 then we move the filters to 1 pixel at a time. When the stride is 2 then we move the filters to 2 pixels at a time and so on.
Padding:
It refers to the amount of pixels added to an image when it is being processed by the filter/kernel of a CNN.
Deep Neural Networks:
A deep neural network (DNN) is an artificial neural network(ANN) with a certain level of complexity, a neural network with multiple layers between the input and output layers. Deep neural networks use sophisticated mathematical modeling to process data in complex ways.
Multilayer Perceptrons:
It is a class of feedforward artificial neural network(ANN).It consists of three or more layers (an input and an output layer with one or more hidden layers) of non-linearly activating nodes. Multilayer perceptrons are often applied to supervised learning problems, they train on a set of input-output pairs and learn to model the correlation (or dependencies) between those inputs and outputs.
Overfitting:
It is the result of an analysis that corresponds too closely or exactly to a particular set of data, and may therefore fail to fit additional data or predict future observations reliably. It happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data.
Filters:
Filters (also known as kernels and convolutional neurons), represented by a vector of weights are small units that are applied across the data through a sliding window.
For queries, feel free to write in the comment💬 section below. You can connect with me on LinkedIn !!
Thank you for reading! Have a great day ahead😃
