Master Recurrent Neural Networks (RNNs) and Sequence Modeling, the core technologies behind applications like natural language processing (NLP), text generation, speech recognition, and time-series forecasting.
This course is designed to give you both a strong conceptual understanding and hands-on experience in building deep learning models for sequential data.
You will begin with an introduction to sequence modeling and RNNs, understanding how these models process time-dependent and sequential data differently from traditional neural networks.
Next, you’ll explore the RNN architecture and Backpropagation Through Time (BPTT), which enables learning from sequences and temporal patterns.
The course then covers advanced RNN variants:
You will also learn text preprocessing and word embeddings, essential for converting textual data into numerical representations for AI models.
Further, the course introduces sequence-to-sequence (Seq2Seq) models, widely used in applications like machine translation, chatbots, and speech systems.
Finally, you will apply all concepts in a hands-on project, where you will build a model for text generation or sentiment analysis, simulating real-world AI applications.
Convolutional Neural Networks (CNNs) with Python
