Machine Learning

In this tutorial, you’ll learn about the Cross-Entropy Loss Function in PyTorch for developing your deep-learning models. The cross-entropy loss function is an important criterion for evaluating multi-class classification models. This tutorial demystifies the cross-entropy loss function, by providing a comprehensive overview of its significance and implementation in deep learning. By the end of this […]

Getting Started with PyTorch Welcome to the “Getting Started with PyTorch” section! This module is your launchpad into the world of PyTorch, the dynamic open-source framework for deep learning. From grasping core tensor concepts to constructing your initial neural network, this section equips you with vital skills for your AI and machine learning endeavors. Let’s

In this tutorial, you’ll learn about how to use transfer learning in PyTorch to significantly boost your deep learning projects. Transfer learning is about leveraging the knowledge gained from one task and applying it to another. This allows you to cut down your training time and improve the performance of your deep-learning models. This tutorial

In this tutorial, you’ll learn about how to use PyTorch transforms to perform transformations used to increase the robustness of your deep-learning models. In deep learning, the quality of data plays an important role in determining the performance and generalization of the models you build. PyTorch transforms are a collection of operations that can be

In this guide, you’ll learn about the PyTorch autograd engine, which allows your model to compute gradients. In deep learning, a fundamental algorithm is backpropagation, which allows your model to adjust its parameters according to the gradient of the loss function with respect to the given parameter. Because of how important backpropagation is in deep

In this guide, you’ll learn all you need to know to work with PyTorch tensors, including how to create them, manipulate them, and discover their attributes. PyTorch tensors are a fundamental building block of deep-learning models. Understanding how tensors work will make learning how to build neural networks much, much easier. By the end of

In this guide, you’ll learn how to develop convolution neural networks (or CNN, for short) using the PyTorch deep learning framework in Python. Convolution neural networks are a cornerstone of deep learning for image classification tasks. Understanding how to develop a CNN in PyTorch is an essential skill for any budding deep-learning practitioner. By the

In this tutorial, you’ll learn how to create linear regression models in PyTorch. Linear models are one of the foundational building blocks of deep learning models. Understanding how to build linear models in PyTorch can allow you to solve many different types of problems. By the end of this tutorial, you’ll have learned the following:

In this tutorial, you’ll learn about the PyTorch Dataset class and how they’re used in deep learning projects. PyTorch encapsulates much of its workflow in custom classes, such as DataLoaders and neural networks. Datasets are one of these classes and help us organize and load data for training and inference tasks. By the end of

Activation functions play a vital role in the realm of deep learning. They’re like the secret sauce that adds flavor and complexity to our neural networks. Think of them as the superheroes that bring life to our models, enabling them to learn intricate patterns and make mind-blowing predictions. Neural networks are all about making connections

In this tutorial, you’ll learn how to use PyTorch for an end-to-end deep learning project. Learning PyTorch can seem intimidating, with its specialized classes and workflows – but it doesn’t have to be. This tutorial will abstract away the math behind neural networks and deep learning. Instead, we’ll focus on learning the mechanics behind how