Computer Vision

A complete technical breakdown of Fast R-CNN and Faster R-CNN, covering RoI Pooling, quantization effects, Region Proposal Networks, anchor boxes, IoU labeling, multi-task loss, and why replacing Selective Search with RPN transformed object detection into a fully end-to-end trainable two-stage architecture.

Unlock the mechanics of Object Detection with our deep dive into R-CNN. Moving beyond simple image classification, this guide explores how machines localize objects using Bounding Boxes, Selective Search, and Support Vector Machines. Whether you are calculating IoU or understanding the transition from sliding windows to smart proposals, this article covers the complete R-CNN architecture and evaluation metrics.

Attention mechanisms revolutionized NLP, but how do they differ? We deconstruct the architecture of Bahdanau (Additive) and Luong (Multiplicative) attention. From calculating alignment weights to updating context vectors, dive into the step-by-step math. Understand why Luong's dot product approach often outperforms Bahdanau's neural network method and how decoder states drive the prediction process.

Transformers are the foundation of modern AI systems like ChatGPT, BERT, and Vision Transformers. This article explains what Transformers are, how self-attention works, their historical evolution, impact on NLP and generative AI, advantages, limitations, and future directions—all explained clearly from first principles.

Training deep learning models from scratch is often impractical due to massive data requirements and long training times. This article explains why these challenges exist and how pretrained models and transfer learning enable faster, more efficient model development with limited data and resources.

Pretrained models in CNNs allow us to reuse knowledge learned from large datasets like ImageNet to build accurate computer vision systems with less data, time, and computational cost. This article explains pretrained models, ImageNet, ILSVRC, AlexNet, and the evolution of modern CNN architectures.

This blog explains the difference between Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) using intuitive examples. It covers how images are processed, why CNNs scale better with fewer parameters, and how spatial features are preserved, making CNNs the preferred choice for image-based tasks.

This blog explains the complete CNN architecture, starting from convolution, activation, and pooling, and then dives deep into the classic LeNet-5 architecture. It covers layer-by-layer dimensions, design choices, activation functions, and why LeNet-5 became the foundation of modern convolutional neural networks.

Pooling is a fundamental operation in Convolutional Neural Networks that reduces feature map size, controls memory usage, and addresses translation variance. This article explains why pooling is needed after convolution, how max pooling works step by step, pooling on volumes, and the advantages and limitations of different pooling techniques in deep learning models.

Padding and strides are key concepts in convolutional neural networks that control spatial dimensions and efficiency. This article explains why padding preserves boundary information and spatial size, how zero padding works mathematically, and how stride reduces feature map resolution. With clear intuition and formulas, it shows how padding maintains detail while strided convolution enables efficient downsampling.

Convolution is the core operation behind Convolutional Neural Networks (CNNs) that enables machines to understand images. This blog explains convolution from first principles, starting with how images are represented in memory and progressing to edge detection, feature maps, RGB convolution, and the role of multiple filters. Through intuitive explanations and practical examples, you will gain a clear understanding of how CNNs extract hierarchical features from images.

Convolutional Neural Networks (CNNs) are inspired by how our visual cortex understands shapes, edges, and patterns. This blog explains CNNs with simple intuition, real experiments like the Hubel & Wiesel cat study, the evolution from the Neocognitron to modern deep learning models, and practical applications in computer vision.