Sequence Models Course Overview 🎯

Welcome to the Sequence Models course within the Deep Learning Specialization! This curriculum focuses on building neural networks to process sequential data like text, speech, and time series.

📘 Course Highlights

• Core Concepts: Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRUs), and Attention Mechanisms
• Applications: Machine translation, text generation, speech recognition, and sequence-to-sequence tasks
• Tools: TensorFlow, PyTorch, and Keras for implementing models

📚 Key Modules

1. Introduction to Sequences

   • Understanding sequence data and its challenges
   • Basics of temporal dynamics in neural networks

2. RNNs & LSTMs

   • Forward propagation in RNNs
   • Vanishing gradient problem and LSTM solutions

3. Attention Models

   • Self-attention and transformer architectures
   • Applications in natural language processing (NLP)

4. Practical Projects

   • Build a chatbot using sequence-to-sequence models
   • Generate text with LSTM-based language models

🔗 Expand Your Knowledge

For deeper insights into advanced techniques, visit our Deep Learning Specialization course.