Deep Learning

Layer Normalisation is a core component of modern Transformer architectures. This article explains normalization fundamentals, internal covariate shift, why batch normalization fails in self-attention, and how layer normalization works mathematically inside Transformers—step by step with clear examples.

Self-attention models lack an inherent sense of word order. This article explains positional encoding in Transformers from first principles, showing how sine–cosine functions encode absolute and relative positions efficiently and enable sequence understanding.

Multi-head attention addresses a key limitation of self-attention by enabling Transformers to capture multiple semantic perspectives simultaneously. This article explains the intuition, working mechanism, dimensional flow, and original Transformer implementation of multi-head attention using clear examples and mathematical reasoning.

This blog explains why self-attention qualifies as an attention mechanism and why the term “self” is used. By revisiting encoder–decoder attention, Luong attention, and alignment scores, we build a clear intuition for how self-attention works within a single sequence.

This post explains self-attention using geometric intuition. By visualizing embeddings, dot products, scaling, and weighted vector sums, we see how contextual embeddings shift based on surrounding words and capture meaning relative to context.

Scaled dot-product attention is a core component of Transformer models, but why do we divide by √dₖ before applying softmax? This article explains the variance growth problem in high-dimensional dot products, the role of scaling in stabilizing softmax, and the mathematical intuition that makes attention training reliable and effective.

Self-attention is the core idea behind Transformer models, yet it is often explained as a black box. In this article, we build self-attention from first principles—starting with simple word interactions, moving through dot products and softmax, and finally introducing query, key, and value vectors with learnable parameters. The goal is to develop a clear, intuitive, and mathematically grounded understanding of how contextual embeddings are generated in Transformers.

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.

The attention mechanism solves the core limitation of traditional encoder–decoder models by dynamically focusing on relevant input tokens at each decoding step. This article explains why attention is needed, how alignment scores and context vectors work, and why attention dramatically improves translation quality for long sequences.

Sequence-to-sequence models form the foundation of modern neural machine translation. In this article, I explain the encoder–decoder architecture from first principles, covering variable-length sequences, training with teacher forcing, backpropagation through time, prediction flow, and key improvements such as embeddings and deep LSTMs—using intuitive explanations and clear diagrams.

Discover the fascinating journey of Artificial Intelligence from simple Sequence-to-Sequence tasks to the rise of Large Language Models. This guide traces the evolution from Recurrent Neural Networks (RNNs) and the Encoder-Decoder architecture to the revolutionary Attention Mechanism, Transformers, and the era of Transfer Learning that gave birth to BERT and GPT.

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.

Standard Gradient Descent is rarely enough for modern neural networks. In this guide, we trace the evolution of optimization algorithms—from the 'look-ahead' mechanism of Nesterov Accelerated Gradient to the adaptive learning rates of Adagrad and RMSProp. Finally, we demystify Adam to understand why it combines the best of both worlds.