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Language Modelling

This course explores the theory, techniques, and applications of language modeling, a fundamental component of natural language processing (NLP) and artificial intelligence.

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Course Detail

Students will learn how machines can understand, generate, and predict human language by building probabilistic and neural models.

Course Features

Terminology science and linguistic standardization
Introduction to Language Modeling
Probability and Statistical Methods
Transformers and Attention Mechanisms
Pre-training and Fine-tuning

Sakary Moore

NLP Engineer

A computational linguist and research engineer with a strong focus on **natural language processing, semantic web**, and knowledge engineering.

20 hours on-demand video
Full Lifetime Access
Access on Mobile and TV
Certificate of Completion

Course Content

11 sections • 28 lecture • 19h 33m total length

N-gram models predict the next word in a sequence using the probabilities of preceding words. They are simple statistical models based on word co-occurrence counts. Limitations include sparsity and inability to capture long-range dependencies.

Feedforward models use fixed-size context windows to predict the next word. They learn distributed word representations (embeddings) and capture some semantic patterns. However, they struggle with long sequences due to limited context size

terms for consistency and accuracy. Understand the characteristics of effective, user-friendly terminology.

RNNs process sequences one step at a time, maintaining a hidden state to capture context. They are capable of modeling dependencies across variable-length sequences. Training challenges include vanishing and exploding gradients for long sequences.

LSTMs are a type of RNN designed to remember long-term dependencies. They use gating mechanisms to control information flow and avoid gradient problems. LSTMs are widely used for tasks like text generation and machine translation.

GRUs are a simplified version of LSTMs with fewer gates, making them faster to train. They also handle long-term dependencies effectively while using less computation. GRUs are popular in sequence modeling tasks like speech and text processing.

Transformers use self-attention to capture relationships between all words in a sequence simultaneously. They allow for highly parallelizable training and handle long-range dependencies efficiently. They form the backbone of modern large language models like GPT and BERT.

Pre-trained models are trained on massive text corpora and fine-tuned for specific tasks. BERT focuses on bidirectional context understanding, while GPT is generative. T5 unifies tasks by converting them into a text-to-text format for versatile applications.