The PyTorch library is one of the most popular machine learning libraries in Python. It is used for computer vision and natural language. Check out our Python PyTorch tutorials.
PyTorch view() returns a tensor with a new shape that shares the original’s data, so it copies nothing. The new shape must hold the same number of elements. Pass -1 for one dimension and PyTorch works it out: I ran these examples with PyTorch 2.14.0+cpu on Python 3.12.5; a CPU build is enough for everything … Read more >>
nn.Conv1d is the 1D convolution layer in PyTorch, for signals, time series and text. It expects a 3D tensor of (batch, channels, length): Nearly every problem with it is a shape problem, and the two shapes to keep straight are that input and the weight, which is (out_channels, in_channels, kernel_size). Runs below are on PyTorch … Read more >>
In this article, I’ll show you how to add dimensions to PyTorch tensors using various methods. After working with PyTorch for over a decade, I’ve found that understanding how to manipulate tensor dimensions is crucial for building effective neural networks. Dealing with tensor dimensions might seem intimidating at first, but it’s quite simple once you … Read more >>
Working with PyTorch tensors often requires changing their shapes to fit specific neural network architectures. I’ve been using PyTorch for years in various deep learning projects, and reshaping tensors is something I do almost daily. While building a computer vision model to classify American landmarks, I needed to transform my batch of images to meet … Read more >>
nn.Conv2d is the 2D convolution layer in PyTorch. You give it how many channels come in, how many should go out, and how big the kernel is, and it builds the weights for you. It expects a 4D tensor shaped (batch, channels, height, width), and almost every problem people hit with it is a shape … Read more >>
Recently, I was working on a deep learning project where I needed to decide between JAX and PyTorch. As someone who’s been developing in Python for over a decade, I’ve witnessed the evolution of these frameworks firsthand. The decision wasn’t easy. Both frameworks have their strengths and use cases. In this tutorial, I will share … Read more >>
Recently, I worked on a deep learning project where my neural network struggled with the “dying ReLU” problem. As I researched solutions, I found that the Leaky ReLU activation function provided a simple yet effective fix. The issue is, traditional ReLU functions can cause neurons to “die” during training when they only output zeros. This … Read more >>
Recently, I was working on a project that required processing sequential data for a natural language processing task. The issue is that traditional neural networks don’t handle sequential data well. So we need a specialized approach. In this article, I will cover how to implement and use Recurrent Neural Networks (RNNs) in PyTorch with practical … Read more >>
Recently, I was working on a deep learning project where I needed to understand and implement fully connected layers in PyTorch. The fully connected layer, also known as a linear layer, is a fundamental building block in neural networks. In this article, I will share my experiences with PyTorch’s fully connected layers and demonstrate how … Read more >>
While working on a project that required classifying handwritten digits, I found the MNIST dataset to be perfect for this task. Using PyTorch made implementing neural networks surprisingly simple. In this article, I’ll walk you through creating, training, and testing a neural network on the MNIST dataset using PyTorch. We’ll start with the basics and … Read more >>