Part 1: RNN vs LSTM Explained | Why LSTM Solves the Vanishing Gradient Problem | Harish Sharma

Recurrent Neural Networks (RNNs) and Long Short-Term Memory networks (LSTMs) are fundamental architectures in deep learning, especially for sequential data like time series, text, and speech. But what really makes LSTM different from a standard RNN?

In this video, we break down:

How basic RNNs work

The vanishing gradient problem in RNNs

Why RNNs struggle with long-term dependencies

How LSTM architecture solves these issues using gates and memory cells

Key differences between RNN and LSTM with intuitive explanations

This tutorial is perfect for machine learning beginners, deep learning students, and anyone preparing for interviews or looking to strengthen their understanding of neural networks.

Видео Part 1: RNN vs LSTM Explained | Why LSTM Solves the Vanishing Gradient Problem | Harish Sharma канала Learn With Baba