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 >>
While building an online shopping app in TypeScript, you first use namespaces to group code like Customer, Product, and Order in one file. It works fine for small projects. However, as the app grows, you can switch to modules to split code into separate files using export and import, making it easier to manage. TypeScript, … 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 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 >>
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 involved implementing a neural network for image classification. One of the fundamental components I needed was the linear layer in PyTorch. This module is essential for creating fully connected layers in neural networks, but many beginners find it challenging to implement correctly. In this guide, I’ll … Read more >>
Throughout my more than ten years as a Python developer, I have worked with various optimization algorithms for deep learning models. Among these, Adam has consistently been one of my preferred choices due to its efficiency and reliability. Adam (short for Adaptive Moment Estimation) combines the best aspects of other popular optimizers like AdaGrad and … Read more >>
Recently, I was working on a deep learning project where I needed to train a neural network for image classification. When selecting a loss function, I found that Cross Entropy Loss was recommended for multi-class classification problems, but I wasn’t entirely clear on how to implement it properly in PyTorch. Cross-entropy loss is a widely … Read more >>
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