Intermediate

RNN: Learning from Sequences

Name: RNN: Learning from Sequences
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Recurrent Neural Networks (RNN) are a type of neural network designed to process sequential data. They use feedback loops to retain information from previous steps, making them useful for tasks like text generation, speech recognition, and time-series prediction.

4.4

|3h 45m

What's Included

Lessons

3h 45m

Duration

Certificate

What You'll Master

Skills and outcomes you'll walk away with

Module 1: Foundations of Sequence Modeling and Simple RNNs

Module 2: The Mathematics of Backpropagation Through Time (BPTT)

Module 3: Long Short-Term Memory (LSTM) Architectures

Practical Project - 1

Module 4: Gated Recurrent Units (GRU) and Efficiency

Module 5: Sequence-to-Sequence (Seq2Seq) Models

Course Curriculum

13 lessons • 3h 45m total

Module 1: Foundations of Sequence Modeling and Simple RNNs

18m

Module 2: The Mathematics of Backpropagation Through Time (BPTT)

18m

Module 3: Long Short-Term Memory (LSTM) Architectures

19m

Practical Project - 1

11m

Module 4: Gated Recurrent Units (GRU) and Efficiency

17m

Module 5: Sequence-to-Sequence (Seq2Seq) Models

20m

Module 6: Attention Mechanisms in RNNs

21m

Practical Project - 2

12m

Module 7: Advanced RNN Variants and Bidirectionality

18m

Module 8: Generative RNNs and Language Modeling

20m

Module 9: Real-World Applications and Deployment

20m

Practical Project - 3

12m

Module 10: Optimization, Hardware, and the Future

19m

Certification Path

Certification Exam

30 multiple-choice questions • 70% passing score required

Final Project: Advanced Sequence Modeling with LSTMs and GRUs

In this final assignment, students will design and implement two distinct RNN-based models. Part one requires building a many-to-one architecture for sentiment analysis on a provided movie review dataset. Part two involves constructing a many-to-many architecture for multi-step time series forecasting of energy consumption data. Students must demonstrate proficiency in handling vanishing gradients, implementing dropout for regularization, and optimizing sequence length through padding and truncation techniques. A final report must compare the performance of Vanilla RNNs, LSTMs, and GRUs for these specific tasks.

Verified Certificate

Earn a verified PDF certificate with unique verification ID upon completion • ₹299