All Posts

Long Short-Term Memory (LSTM) networks solve the limitations of traditional RNNs through a powerful gating mechanism. This article explains how the Forget, Input, and Output gates work internally, breaking down the math, vector dimensions, and intuition behind cell states and hidden states. A deep, implementation-level guide for serious deep learning practitioners.

LSTM (Long Short-Term Memory) is a powerful neural network architecture designed to handle long-term dependencies in sequential data. In this blog, we explain LSTMs intuitively using a simple story, compare them with traditional RNNs, and break down forget, input, and output gates in a clear, beginner-friendly way.

Recurrent Neural Networks are designed for sequential data, yet they suffer from critical training issues. This article explains the long-term dependency and exploding gradient problems in RNNs using clear intuition, mathematical insight, and practical solutions like gradient clipping and LSTM.

Backpropagation in RNNs is often confusing because a single weight affects the loss through multiple time-dependent paths. In this post, I break down Backpropagation Through Time step by step using a small toy dataset, clearly showing how gradients flow across timesteps and why unfolding the network is necessary.

This guide explains the major types of Recurrent Neural Network (RNN) architectures based on how they map inputs to outputs. It covers Many-to-One, One-to-Many, and Many-to-Many (Seq2Seq) models, along with practical examples such as sentiment analysis, image captioning, POS tagging, NER, and machine translation, helping you understand when and why each architecture is used.

This definitive guide explains why sequential data requires Recurrent Neural Networks, explores the limitations of ANNs, and walks through RNN data formats, architecture, and forward propagation in detail.

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.