In the rapidly evolving field of deep learning, the challenge often lies not just in designing powerful models but also in making them accessible and efficient for practical use, especially on devices ...
GradCAM – Enhancing Neural Network Interpretability in the Realm of Explainable AI
With millions of trainable parameters, neural networks have long been considered black boxes. They can produce stunning results, and we often accept the output with very little understanding as to why ...
Enhancing Medical Multi-Label Image Classification Using PyTorch & Lightning
In the pivotal field of medical diagnostics, swift and accurate image classification plays a crucial role in aiding healthcare professionals' decision-making. The advent of deep learning, coupled with ...
Image Classification using Pre-Trained ImageNet Models in TensorFlow & Keras
In this post, we will learn how to use pre-trained ImageNet models to perform image classification. We have already seen how we can train a simple neural network to classify images from the CIFAR-10 ...
Convolutional Neural Network (CNN): A Complete Guide
Convolutional Neural Network (CNN) forms the basis of computer vision and image processing. In this post, we will learn about Convolutional Neural Networks in the context of an image classification ...
Implementing a CNN in TensorFlow & Keras
In this post, we’ll learn how to implement a Convolutional Neural Network (CNN) from scratch using Keras. Here, we show a CNN architecture similar to the structure of VGG-16 but with fewer layers. We ...
