Deep Dive into Encoder-Decoder Architecture: Theory, Implementation and Applications

Introduction The encoder-decoder architecture represents one of the most influential developments in deep learning, particularly for sequence-to-sequence tasks. This architecture has revolutionized machine translation, speech recognition, image captioning, and many other applications where input and output data have different structures or lengths. In this blog post, we’ll explore: Table of Contents <a name=”fundamentals”></a> 1. Fundamentals […]

Read More

Understanding LSTM Networks with Forward and Backward Propagation Mathematical Intuitions

Developed by : tejask0512 Recurrent Neural Networks Humans don’t begin their thought process from zero every moment. As you read this essay, you interpret each word in the context of the ones that came before it. You don’t discard everything and restart your thinking each time — your thoughts carry forward. Conventional neural networks lack […]

Read More

Deep Learning For NLP Prerequisites

Understanding RNN Architectures for NLP: From Simple to Complex Natural Language Processing (NLP) has evolved dramatically with the development of increasingly sophisticated neural network architectures. In this blog post, we’ll explore various recurrent neural network (RNN) architectures that have revolutionized NLP tasks, from basic RNNs to complex encoder-decoder models. Simple RNN: The Foundation What is […]

Read More

Comprehensive Guide to NLP Text Representation Techniques

Natural Language Processing (NLP) requires converting human language into numerical formats that computers can understand. This guide explores major text representation techniques in depth, comparing their strengths, weaknesses, and practical applications. 1. One-Hot Encoding One-hot encoding is a fundamental representation technique that forms the conceptual foundation for many text representation methods. How It Works One-hot […]

Read More