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