Table of Contents Introduction: What is overfitting? Causes of overfitting: How to detect overfitting? Avoiding the overfitting: Underfitting in machine learning: Causes of underfitting: How to avoid underfitting: Introduction: In machine learning, models are trained on training data to make accurate predictions on test data. The ideal model demonstrates strong performance on test data, indicating […]
Generative adversarial network is a type of neural network used to generate fake data that resembles real data. GANs use two networks are used against each other. In most machine learning tasks input is given to the model and model is trained for predictions based on the training data. In GANs a neural network model […]
A VAE is a generative model used in unsupervised learning for generating new data points similar to the training data. It combines elements of both autoencoders and variational inference. In VAE the input is encoded to latent distribution z typically the normal distribution, generating the mean and standard deviation matrix. Then the sample is taken […]
Autoencoder is a type of neural network that is used to reconstruct the given data. It works by learning the lower dimensional and compressed representations of data and is used to reconstruct the data. The data reconstructed have less noise compared to the input data. Autoencoder consists of two networks named as encoder and decoder […]
Activation functions are an important part of deep learning, used to induce no-linearity in the model’s output. They enable the model to learn non-linearity in data. Without activation function, the equation of the neural network is the linear product of input values and the weights in which bias terms are added. These functions enable deep […]
Stride: refers to the step size at which the convolutional filter/kernel moves across the input image or feature map during the convolution operation. It determines how much the filter shifts horizontally and vertically between successive convolutions. Padding refers to the technique of adding extra zeros around the input image or the feature map before applying […]
A convolutional neural network is a deep learning model that is specifically used for image and video processing. They are used for the matrix dataset in which the learnable filters are passed over the image or matrix to extract the useful features. It preserved the spatial structure of images that is lost in NN as […]