PyTorch

Last Updated on November 29, 2022 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…

Last Updated on November 29, 2022 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…

Last Updated on November 23, 2022 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…

Last Updated on November 23, 2022 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 the focus…

Last Updated on November 15, 2022 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,…

Last Updated on November 15, 2022 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…

Last Updated on November 15, 2022 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…

Train your image classification models with the most popular research datasetPhoto by Ion Fet on UnsplashImageNet is the most popular dataset in computer vision research. The image dataset contains collected images for all sorts of categories found in the WordNet hierarchy. The 168 GB large dataset contains 1.3 million images separated into 1,000 classes with different grains of label resolution. For example, it contains classes of planes and dogs, but also classes of different dog breeds which are even hard to classify…

The Power of PyTorch/XLA and how Amazon SageMaker Training Compiler Simplifies its usePhoto by Patrick Fore on Unsplashby AuthorIn many of our past posts (e.g., here) we have emphasized the importance of managing the cost of training. We are in constant pursuit of ways to increase the runtime performance of our training through an iterative process of 1. profiling our workloads in order to identify performance bottlenecks and resource under-utilization, and 2. optimizing our workloads to remove bottlenecks and increase…

Photo by Rock Staar on UnsplashLearn how to build an entire deep learning pipeline in PyTorchIntroductionIt is no secret that the price of petrol has skyrocketed in the last few months. People are filling up with gasoline the minimum necessary both because of a cost factor and for reasons related to the environment. But have you ever noticed that when you look up on the Internet how much your car should spend on gas to get from point A to point B, the numbers almost never match reality?In this article, I want to develop a…