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 >>
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 >>
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