Recurrent Neural Network RNN Explained Types and ANN vs RNN Difference

Описание: RNN Complete Guide Sequential Data Many to One Many to Many Explained
In this video, we clearly understand Recurrent Neural Networks (RNN) and why they are powerful for handling sequential data.

Here is the GitHub repo link:
https://github.com/switch2ai

You can download all the code, scripts, and documents from the above GitHub repository.

What is RNN?

RNN (Recurrent Neural Network) is a type of neural network designed to process sequential data where order matters.

Examples of sequential data:
• Text
• Time series
• Speech
• Sentences
• Stock prices

Why RNN?

Problems with ANN:

• ANN does not consider order of data
• ANN requires fixed input/output size
• ANN processes data in one single pass

How RNN Solves These Problems

• Data is passed time-step wise
• Output from previous time step is passed to next time step
• Parameter sharing across all time steps
• Can handle variable length input and output

ANN vs RNN Difference

ANN → Fixed input/output size
RNN → Variable length input/output

ANN → Data passed in one go
RNN → Data passed sequentially

ANN → Output passed only to next layer
RNN → Output passed to next time step also

Types of RNN
One to One

Example: House price prediction

One to Many

Example: Image captioning

Many to One

Example: Sentiment analysis

Many to Many

Same input/output length:
• Named Entity Recognition (NER)

Different input/output length:
• Text Summarization
• Machine Translation

By the end of this video, you will understand:

• Why RNN is required for sequence modeling
• How hidden states carry information
• Types of RNN architectures
• Real-world NLP applications

This video is perfect for:

• NLP learners
• Deep Learning beginners
• AI interview preparation
• Machine Learning students

Channel Name: Switch 2 AI


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