Bijay Kumar 
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 >>
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 >>
While building a TypeScript function, you might want to accept an object as a parameter, but you don’t care what properties it has; you want to make sure it’s some object and not a string, number, or null. For example, you are writing a logging function that accepts any object and prints it to the … Read more >>
I was working on a TypeScript project that tracked the status of different cities. There, I got a challenge that the list of city names kept changing, and I didn’t know all the keys ahead of time. At first, I tried using a loose object type, but that quickly led to confusion and potential errors. … Read more >>
When you start a new React project, you might think Should I use JavaScript or try TypeScript? JavaScript is easier to begin with, but TypeScript helps you write cleaner and safer code. Picking the right one can significantly impact how quickly you work, the quality of your code, and the effectiveness of your team collaboration. … 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 >>
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