PyTorch is an open-source deep learning framework based on Python language. It allows you to build, train, and deploy deep learning models, offering a lot of versatility and efficiency. PyTorch is primarily focused on tensor operations while a tensor can be a number, matrix, or a multi-dimensional array. In this tutorial, we will perform some basic operations on one-dimensional tensors as they are complex mathematical objects and an essential part of the PyTorch library. Therefore, before going into the detail […]

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, the lowest intensity number (which is ‘0’) represents black regions in the image while the highest intensity number (which is ‘255’) represents white regions in […]

Derivatives are one of the most fundamental concepts in calculus. They describe how changes in the variable inputs affect the function outputs. The objective of this article is to provide a high-level introduction to calculating derivatives in PyTorch for those who are new to the framework. PyTorch offers a convenient way to calculate derivatives for user-defined functions. While we always have to deal with backpropagation (an algorithm known to be the backbone of a neural network) in neural networks, which […]

In machine learning and deep learning problems, a lot of effort goes into preparing the data. Data is usually messy and needs to be preprocessed before it can be used for training a model. If the data is not prepared correctly, the model won’t be able to generalize well.Some of the common steps required for data preprocessing include: Data normalization: This includes normalizing the data between a range of values in a dataset. Data augmentation: This includes generating new samples […]

Structuring the data pipeline in a way that it can be effortlessly linked to your deep learning model is an important aspect of any deep learning-based system. PyTorch packs everything to do just that. While in the previous tutorial, we used simple datasets, we’ll need to work with larger datasets in real world scenarios in order to fully exploit the potential of deep learning and neural networks. In this tutorial, you’ll learn how to build custom datasets in PyTorch. While […]

Linear regression is a statistical technique for estimating the relationship between two variables. A simple example of linear regression is to predict the height of someone based on the square root of the person’s weight (that’s what BMI is based on). To do this, we need to find the slope and intercept of the line. The slope is how much one variable changes with the change in other variable by one unit. The intercept is where our line crosses with […]

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 outcomes such as price or quantity demand, based on other variables that are known to influence these outcomes. In order to train a linear regression […]

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 learning. Gradient descent is an iterative optimization method used to find the minimum of an objective function by updating values iteratively on each step. With […]

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. This process is repeated with different batches until the whole training data has been processed. Compared to batch gradient descent, the main benefit of this […]