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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 >>
Recently, I was working on a deep learning project where I needed to implement a convolutional neural network (CNN) for image classification. The cornerstone of any CNN is the convolutional layer, and in PyTorch, this is implemented through the nn.Conv2d module. While working with this module, I realized that mastering its parameters and understanding how … 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 >>
While I was debugging a complex CNN architecture, and needed to quickly check the number of parameters and layer shapes. The issue was, PyTorch doesn’t have a built-in summary function like Keras does. After experimenting with different solutions, I found several effective ways to visualize model architecture. In this article, I’ll share five practical methods … Read more >>
When working on binary classification problems in deep learning, choosing the right loss function is crucial. Recently, I was building a sentiment analysis model that needed to classify text as either positive or negative, and PyTorch’s Binary Cross Entropy (BCE) loss function proved to be exactly what I needed. Binary Cross Entropy is a widely … Read more >>
I was working on a deep learning project that required me to efficiently load and batch large datasets for training a neural network. Manually managing data batching, shuffling, and parallel loading can be very tedious and prone to errors. This is where PyTorch’s DataLoader becomes extremely helpful. In this article, I will cover everything you … Read more >>
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