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Using Optimizers from PyTorch

Optimization is a process where we try to find the best possible set of parameters for a deep learning model. Optimizers generate new parameter values and evaluate them using some criterion to determine the best option. Being an important part of neural network architecture, optimizers help in determining best weights, biases or other hyper-parameters that […]

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Mini-Batch Gradient Descent and DataLoader in PyTorch

Mini-batch gradient descent is a variant of gradient descent algorithm that is commonly used to train deep learning models. The idea behind this algorithm is to divide the training data into batches, which are then processed sequentially. In each iteration, we update the weights of all the training samples belonging to a particular batch together. […]

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Implementing Gradient Descent in PyTorch

The gradient descent algorithm is one of the most popular techniques for training deep neural networks. It has many applications in fields such as computer vision, speech recognition, and natural language processing. While the idea of gradient descent has been around for decades, it’s only recently that it’s been applied to applications related to deep […]

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Training a Linear Regression Model in PyTorch

Linear regression is a simple yet powerful technique for predicting the values of variables based on other variables. It is often used for modeling relationships between two or more continuous variables, such as the relationship between income and age, or the relationship between weight and height. Likewise, linear regression can be used to predict continuous […]

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Two-Dimensional Tensors in Pytorch

Two-dimensional tensors are analogous to two-dimensional metrics. Like a two-dimensional metric, a two-dimensional tensor also has $n$ number of rows and columns. Let’s take a gray-scale image as an example, which is a two-dimensional matrix of numeric values, commonly known as pixels. Ranging from ‘0’ to ‘255’, each number represents a pixel intensity value. Here, […]

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