Engineering Clinic

Transfer Learning Transfer learning is a powerful technique in deep learning that allows us to leverage pre-trained models to solve new tasks with limited data. In this blog post, we’ll walk through a practical example of transfer learning using PyTorch. We’ll fine-tune a pre-trained ResNet18 model to classify images of ants and bees from the Hymenoptera dataset, downloaded from Kaggle. By the end, you’ll understand how to set up the dataset, apply data transformations, train the model, and visualize predictions—all with a few lines of code! What is Transfer Learning? Transfer learning involves taking a model trained on a large, general dataset (like ImageNet) and adapting it to a specific task. Instead of training a neural network from scratch, which requires massive data and computing resources, we start with a pre-trained model and tweak it for our needs. This approach is especially useful when working with small datasets, as it reduces training time and the need for extensive lab...

Building AlexNet from Scratch with PyTorch In the world of deep learning, AlexNet holds a special place as a groundbreaking convolutional neural network (CNN) that won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2012. Developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, it demonstrated the power of deep learning and GPUs for image classification. In this blog post, we’ll implement AlexNet from scratch using PyTorch, explore its architecture, and provide a working code example. What is AlexNet? AlexNet is a deep CNN designed to classify images into 1000 categories. It features five convolutional layers, three max-pooling layers, and three fully connected layers, with ReLU activations and dropout for regularization. Its innovative use of large kernels, overlapping pooling, and GPU acceleration made it a milestone in computer vision. Let’s dive into the implementation! Prerequisites Before running the code, ensure you have the following installed:...