Natural Language Processing with Sequence Models
The Natural Language Processing with Sequence models Course, launched in 2025, provides a comprehensive introduction to the field. Each chapter includes video lectures, notes, slides, and quizzes. The course also offers a curated collection of resources such as GitHub repositories, YouTube channels, and Reddit groups.
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📚Chapter1:Recurrent Neural Networks for Language modeling
This Chapter covers the use of neural networks for sentiment analysis, starting with dense layers and embeddings, and moving to traditional and recurrent models. It explains RNNs, their math, cost functions, and real-world applications, then introduces advanced variants like GRUs and bi-directional RNNs for better sequence understanding.
📚Chapter 2- LSTMS and Named Entity Recognition
This module takes you through the journey of sequence models and NER step by step. You’ll start with RNNs and the vanishing gradient problem, then see how LSTMs overcome this limitation and explore their architecture with input, forget, and output gates. From there, you’ll shift to Named Entity Recognition (NER) — first understanding what it is, then learning about data preprocessing for training, and finally discovering how to evaluate models by computing accuracy with masking and prediction.
📚Chapter 3-Siamese Networks
This module takes you through the journey of sequence models and NER step by step. You’ll start with RNNs and the vanishing gradient problem, then see how LSTMs overcome this limitation and explore their architecture with input, forget, and output gates. From there, you’ll shift to Named Entity Recognition (NER) — first understanding what it is, then learning about data preprocessing for training, and finally discovering how to evaluate models by computing accuracy with masking and prediction.