Ensemble methods enhance machine learning models’ uncertainty estimation by aggregating diverse predictions, improving accuracy, and generalization through training multiple models independently.

While there are various methods for uncertainty modeling in neural networks, Monte Carlo (MC) methods are widely used due to their simplicity and ease of implementation, particularly when predicting probabilities or modeling distributions is computationally… Monte Carlo and MC Dropout for neural network Uncertainty modeling

AdaGrad The AdaGrad algorithm individually adjusts the learning rates of all model parameters by scaling them inversely proportional to the square root of the cumulative sum of their past squared gradients. This means that parameters… A Deep Dive into Adaptive Learning Rate Algorithms with PyTorch implementation

Gradient clipping is a technique used to address the problem of exploding gradients in deep neural networks. It involves capping the gradients during the backpropagation process to prevent them from becoming excessively large, which can… Gradient clipping and Pytorch codes

Minibatch learning in neural networks is akin to dancers learning a complex routine by breaking it down into smaller, manageable sections. This approach allows both the dancers and the neural network to focus on incremental… Minibatch learning and variations of Gradient Descent

Initially, the artificial neural network is like a child. It knows almost nothing! So, it needs to learn. Training a neural network involves using a loss function. The loss function allows the neural network to… Activation, Initialization and Training a Neural network

The idea of neural networks is inspired by the structure and functioning of a brain, where interconnected neurons process and transmit information through complex networks. Neural networks have various applications, such as:Generating and telling jokes… A Comical Introduction to Neural Network