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Introduction to CryptographyHistorical OverviewCryptography has a long history dating back to ancient civilizations such as Caesar and Spartan. In the modern era, cryptography played a crucial role during WWII, with the development of the Enigma machine by the Germans and efforts by Allied cryptanalysts to break its codes.Basic Cryptographic Concepts and TerminologyCryptography is the science of..

NLP Applications and Advanced Topics Sentiment analysis: Sentiment analysis is the process of determining the sentiment or opinion expressed in a piece of text. It involves classifying the sentiment as positive, negative, or neutral. Sentiment analysis is commonly used in social media monitoring, customer feedback analysis, and market research. Named Entity Recognition (NER): Named E..

Advanced NLP Models Transformer architecture: The Transformer architecture, introduced by Vaswani et al. in the paper "Attention is All You Need," is a neural network architecture based entirely on self-attention mechanisms without recurrent or convolutional layers. It has been highly successful in NLP tasks due to its parallelizable nature, scalability, and ability to capture long-range d..

Sequence-to-Sequence Models Introduction to sequence-to-sequence (Seq2Seq) architecture: Sequence-to-sequence (Seq2Seq) models are a type of neural network architecture designed to handle input and output sequences of variable lengths. They consist of an encoder network that processes the input sequence and a decoder network that generates the output sequence. Seq2Seq models are widely use..

Language Modeling Introduction to language models: Language models are statistical models that aim to predict the probability of a sequence of words in a given context. Two common types of language models are: n-gram models: These models predict the next word in a sequence based on the occurrence of preceding n-1 words. They are simple and efficient but suffer from the curse of ..

Text Representation Bag-of-Words model and its limitations: The Bag-of-Words (BoW) model represents text data by counting the frequency of words in a document. It disregards the order and structure of words, treating each document as a set of words. Limitations of the BoW model include: Lack of semantic meaning: The BoW model does not capture the semantic relationships between w..