Deep Learning Complete Course | Part 3| RNN implementation.
Instructor – Akarsh Vyas
Welcome back!
In this video, we take the next step in Deep Learning and dive into Recurrent Neural Networks (RNNs) the models that allow neural networks to understand sequential and time-based data.
After mastering ANN and CNN, this session completes a crucial part of Deep Learning by introducing architectures designed for memory, context, and sequence learning.
You can download the code and datasets from here:
Code files and Dataset – https://github.com/AkarshVyas/Next_word_prediction
All the notes of our classes are here:
Notes – https://drive.google.com/file/d/1Cykev1PzEEMmU3Unif_HBxKtrZUwzgB8/view?usp=sharing
Check out our course - https://www.sheryians.com/courses/courses-details/Data%20Science%20and%20Analytics%20with%20GenAI
Here’s what you’ll learn in this Deep Learning Part 3:
Why ANNs and CNNs fail on sequential data
Introduction to Recurrent Neural Networks (RNNs) and how they work
Understanding vanishing and exploding gradient problems
LSTM (Long Short-Term Memory) — gates, memory cells, and intuition
GRU (Gated Recurrent Units) and how they differ from LSTMs
Comparison between RNN vs LSTM vs GRU
Step-by-step architecture explanation with real examples
Hands-on projects using RNN, LSTM, and GRU
Implementing sequence models using TensorFlow / Keras
Instructor – Akarsh Vyas
Welcome back!
In this video, we take the next step in Deep Learning and dive into Recurrent Neural Networks (RNNs) the models that allow neural networks to understand sequential and time-based data.
After mastering ANN and CNN, this session completes a crucial part of Deep Learning by introducing architectures designed for memory, context, and sequence learning.
You can download the code and datasets from here:
Code files and Dataset – https://github.com/AkarshVyas/Next_word_prediction
All the notes of our classes are here:
Notes – https://drive.google.com/file/d/1Cykev1PzEEMmU3Unif_HBxKtrZUwzgB8/view?usp=sharing
Check out our course – https://www.sheryians.com/courses/courses-details/Data%20Science%20and%20Analytics%20with%20GenAI
Here’s what you’ll learn in this Deep Learning Part 3:
Why ANNs and CNNs fail on sequential data
Introduction to Recurrent Neural Networks (RNNs) and how they work
Understanding vanishing and exploding gradient problems
LSTM (Long Short-Term Memory) — gates, memory cells, and intuition
GRU (Gated Recurrent Units) and how they differ from LSTMs
Comparison between RNN vs LSTM vs GRU
Step-by-step architecture explanation with real examples
Hands-on projects using RNN, LSTM, and GRU
Implementing sequence models using TensorFlow / Keras
