Complete Machine Learning Course with Projects | Learn ML Step-by-Step - 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