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 >>
Recently, I was working on a deep learning project where my model was performing great on the training data but poorly on the validation set. The issue was, my model was overfitting. This is where early stopping comes to the rescue! In this article, I will show you how to implement early stopping in PyTorch … Read more >>
In TypeScript, module loading is a fundamental concept. With the advent of ECMAScript 2015 (ES6), the import statement was introduced as a modern alternative to the traditional require function used in Node.js. In this blog, we will discuss a detailed comparison between require and import, their usage in TypeScript, and practical examples to help you … Read more >>
Over the past six years, I’ve worked with TypeScript and witnessed its evolution, incorporating many powerful features. One operator that significantly improved type checking in my projects is the satisfies operator introduced in TypeScript 4.9. This operator has been a game-changer for ensuring type correctness while preserving type information. In this article, we will learn … Read more >>
Recently, I was working on a TypeScript project where I needed to implement utility functions that didn’t require an instance of a class. As a solution for this, I decided to use the TypeScript static methods. To build TypeScript applications and write more efficient, organized code, understanding static methods is essential. In this article, I’ll … Read more >>
As a TypeScript developer with over six years of experience, I’ve worked on numerous projects where even the simplest operations, such as adding two numbers, must be implemented correctly. While adding numbers might seem trivial, understanding the TypeScript approach to doing so opens doors to improved type safety and code quality. In this article, I’ll … Read more >>
In my decade-plus career as a Python developer, I’ve found that converting PyTorch tensors to NumPy arrays is a fundamental skill when working with deep learning projects. This conversion is crucial for leveraging PyTorch’s computational power and NumPy’s data manipulation capabilities. In this article, I’ll share various methods to convert PyTorch tensors to NumPy arrays, … Read more >>
Recently, I was working on a deep learning project, and my model was taking an excessively long time to converge. The training process was frustratingly slow, and the accuracy wasn’t improving as I had hoped. That’s when I decided to implement Batch Normalization, a technique that significantly enhanced my model’s performance and reduced training time. … Read more >>
Recently, I worked on a deep learning project that required me to deploy a pre-trained PyTorch model in a production environment. I encountered challenges loading the PyTorch models correctly, especially when dealing with various model architectures and saving formats. In this tutorial, I will cover multiple ways to load PyTorch models (using torch.load, state dictionaries, … Read more >>