Natural Language Processing - An introduction

The Lecture - Predicting the next words

https://quantinar.com/course/908/natural-language-processing

Comprehensive NLP coverage from statistical foundations to advanced transformers, including tokenization, neural networks, pre-trained models, and ethical implications. 

https://quantinar.com/course/922/nlp-predicting-the-next-word

Techniques for next-word prediction using n-grams, neural networks, and transformer models to understand language patterns.

https://quantinar.com/course/921/nlp-the-transformer-revolution

How transformer architecture revolutionized NLP through parallel computation and simultaneous word relationship capture, featuring GPT and BERT.

https://quantinar.com/course/920/nlp-advanced-transformer

Deep dive into modern transformer architectures, covering large language models, emergent abilities, scaling laws, and practical implementation.

https://quantinar.com/course/919/nlp-ethics-and-future-directions

Ethical challenges in NLP including bias, fairness, privacy, and responsible AI development with societal impact considerations.

https://quantinar.com/course/918/nlp-efficiency-and-deployment

Model compression, quantization, and optimization strategies for deploying large language models on resource-limited devices.

https://quantinar.com/course/917/nlp-fine-tuning-and-prompt-engineering

Customizing language models for specific tasks using techniques like LoRA and instruction tuning.

https://quantinar.com/course/916/nlp-decoding-strategies

Text generation methods including greedy search, beam search, and techniques for controlling output quality and diversity.

https://quantinar.com/course/915/nlp-tokenization-and-subword-models

Tokenization techniques like byte-pair encoding (BPE) and their impact on model performance and vocabulary handling.

https://quantinar.com/course/914/nlp-pre-trained-language-models

Understanding BERT and similar models trained on massive datasets that can be fine-tuned with minimal additional data.

https://quantinar.com/course/912/nlp-sequence-to-sequence-models

Neural machine translation and encoder-decoder architectures for applications like translation, chatbots, and summarization.

https://quantinar.com/course/911/nlp-recurrent-neural-networks 

RNN architectures for processing sequential text data, their memory capabilities, challenges, and applications.

https://quantinar.com/course/910/nlp-neural-language-models

Neural networks for understanding word meanings through embeddings and semantic vector representations.

https://quantinar.com/course/909/foundations-and-statistical-language-models

Statistical methods for language prediction including n-gram models and probability distributions.

https://quantinar.com/course/907/natural-language-processing-introduction

Theoretical foundations of next-word prediction from statistical models through neural architectures including embeddings and RNNs.

📘 NLP Learning App – Chapter Navigator

This app provides a structured and interactive way to explore major developments in natural language processing (NLP). It is designed for students, researchers, and professionals who are interested in understanding how machines process and generate human language.

Each chapter introduces a different stage in the evolution of NLP systems, from early statistical models to current large-scale neural architectures. Users can navigate freely or follow the chapters in order, depending on their goals.

🔹 What You Will Learn

Chapter 1 – The Statistical Era
Introduction to early methods such as n-grams, word frequencies, and probabilistic models like Hidden Markov Models (HMMs).

Chapter 2 – Neural Networks & Embeddings
Covers the transition to neural models and distributed word representations (e.g. Word2Vec, GloVe) that allow systems to capture similarity between words.

Chapter 3 – Sequential Models & Context
Explains how recurrent neural networks (RNNs), LSTMs, and GRUs can process sequences and learn dependencies in text.

Chapter 4 – The Transformer Architecture
Introduces the transformer model, self-attention mechanism, and encoder-decoder architecture, which are the basis of many modern NLP systems.

Chapter 5 – Applying the Foundations: Text Classification
Shows how the above methods are used in real applications such as sentiment analysis, spam detection, and document labeling.

Chapter 6 – Generative Models
Focuses on how language models can generate text, including autoregressive models like GPT and masked language models like BERT.

Chapter 7 – Text Summarization
Explores how generative models are applied to produce shorter versions of longer texts, using both extractive and abstractive techniques.

Chapter 8 – Large Language Models (LLMs)
Covers the latest developments in models like GPT-3 and beyond, discussing scale, capabilities, limitations, and emerging business use cases.

🔹 Key Features

• Modular Access: Jump directly to any chapter that matches your interests or learning needs.

• Practical Focus: Each topic includes real-world examples and common use cases.

• Designed for Learning: Clear structure and terminology make the material accessible to users from different backgrounds.

🔹 Who Should Use This

This tool is useful for:

• Graduate and advanced undergraduate students in data science, computer science, business, or economics

• Researchers and educators interested in the structure and function of modern NLP systems

• Professionals working in analytics, finance, or policy, where language models are increasingly applied