Bijay Kumar 
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 >>
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 I needed to evaluate a PyTorch model’s performance on a test dataset. I realized that many beginners don’t fully understand the importance of putting a model in evaluation mode before testing it. In this article, I will guide you through everything you need to know … 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 >>
Recently, I was working on a deep learning project that required training a neural network for regression tasks. The issue is that choosing the right loss function is crucial for model performance. In this tutorial, I will cover everything you need to know about PyTorch’s MSELoss function, from basic implementation to advanced techniques. So let’s … Read more >>
While working on a TypeScript project, I had to deal with data coming from an external source, and I wasn’t sure what type it would be. At first, I used any type to make things easier, but it quickly caused errors that TypeScript didn’t catch. That’s when I looked into the unknown type. It seemed … Read more >>
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