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.
Preparing for a PyTorch interview can feel challenging, especially with so many technical topics to review. This article offers a structured way to study the core concepts, from tensor operations to advanced features like autograd and model optimization. It helps anyone strengthen their understanding of PyTorch fundamentals and demonstrate practical knowledge in real interview situations. … Read more >>
In my decade-plus experience with Python and deep learning frameworks, I’ve found that understanding activation functions is crucial for building effective neural networks. Today, I want to share my knowledge about PyTorch’s nn.Sigmoid() function, a fundamental component that I use frequently in my machine learning projects. The sigmoid function converts raw model outputs into probabilities … Read more >>
In my decade-plus journey as a Python developer, I’ve witnessed the evolution of deep learning frameworks, with PyTorch emerging as one of the most useful tools in the field. When building neural networks, activation functions play a crucial role in introducing non-linearity to our models. The hyperbolic tangent function, commonly known as TanH, is one … Read more >>
torch.softmax turns raw model scores into probabilities that add up to 1: Two things go wrong with it constantly. Picking the wrong dim normalises across the batch instead of across the classes, and applying softmax before CrossEntropyLoss applies it twice. Both are demonstrated below, on Python 3.12.5, PyTorch 2.14.0+cpu. Using softmax in PyTorch Every value … Read more >>
As a Python developer with over a decade of experience, I’ve worked extensively with image processing libraries. PyTorch’s image manipulation capabilities have become indispensable in my deep learning projects. A client recently asked me to create an image classification model to identify various American landmarks. The dataset included images of different sizes, ranging from small … Read more >>
torch.cat joins a sequence of tensors along a dimension that already exists. The shapes have to match everywhere except that one dimension, and the result never gains an axis. That last part is the entire difference between torch.cat and torch.stack, which does add an axis. The runs below are on PyTorch 2.14.0 on Python 3.12.5, … Read more >>
As a Python developer with over a decade of experience in deep learning frameworks, I’ve found PyTorch’s tensor manipulation functions to be incredibly useful yet sometimes overlooked. Among these functions, torch.stack() is one that deserves special attention. When I first started building neural networks, I often struggled with combining multiple tensors efficiently. That’s when I … Read more >>
Have you ever been in the middle of coding a neural network and needed to create a placeholder tensor to store results? I know I have, countless times. After working with PyTorch for over a decade, creating empty tensors has become second nature to me. When I first started, this seemingly simple task caused me … Read more >>
In my decade-plus journey as a Python developer, I’ve found that reshaping tensors is a crucial operation when building neural networks. One of the most common reshaping operations I perform is flattening multi-dimensional data into a 1D or 2D tensor. PyTorch’s Flatten layer is a simple yet useful tool that I use regularly in my … Read more >>
Over my decade-plus journey as a Python developer, I’ve witnessed the evolution of deep learning frameworks firsthand. When it comes to processing 3D data, such as medical scans, video sequences, or volumetric imagery, PyTorch’s Conv3d has been my go-to tool. I recall my first project, analyzing brain MRI scans, where I struggled with traditional 2D … Read more >>
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