Sequence Models Course Syllabus

Overview: This course provides a hands-on introduction to sequence models in deep learning, with a focus on natural language processing applications. Over approximately 37 hours, learners will progress through a series of modules covering foundational and advanced topics, including RNNs, word embeddings, attention mechanisms, and transformer models. The course includes practical coding assignments and ends with a final project, enabling learners to apply their knowledge to real-world NLP tasks. Designed for self-paced learning, it offers lifetime access and a certificate upon completion.

Module 1: Recurrent Neural Networks

Estimated time: 11 hours

• Introduction to RNNs and their architectures
• Understanding Long Short-Term Memory (LSTM) networks
• Exploring Gated Recurrent Unit (GRU) models
• Backpropagation through time and vanishing gradient problem

Module 2: Natural Language Processing & Word Embeddings

Estimated time: 9 hours

• Understanding word embeddings in NLP
• Implementing the word2vec model
• Working with GloVe word embeddings
• Applying embeddings to language tasks

Module 3: Sequence Models & Attention Mechanism

Estimated time: 9 hours

• Introduction to sequence-to-sequence models
• Understanding the attention mechanism
• Applying attention to machine translation

Module 4: Transformer Models & Hugging Face

Estimated time: 8 hours

• Understanding transformer architectures
• Advantages of transformers over RNNs
• Using Hugging Face libraries for NLP

Module 5: Applications of Sequence Models

Estimated time: 6 hours

• Character-level language modeling with RNNs
• Sequence generation tasks
• Named Entity Recognition using Hugging Face
• Question Answering systems

Module 6: Final Project

Estimated time: 10 hours

• Build a sequence model for a real-world NLP task
• Apply word embeddings and attention or transformer components
• Submit trained model and project report

Prerequisites

• Basic understanding of machine learning concepts
• Familiarity with Python programming
• Experience with deep learning fundamentals recommended

What You'll Be Able to Do After

• Build and train RNNs, LSTMs, and GRUs
• Implement word embeddings for NLP applications
• Apply attention mechanisms in sequence-to-sequence models
• Use Hugging Face to perform NER and question answering
• Create models for language modeling and sequence generation