RNN ( Recurrent Neural Network ) in Deep learning
In this session, we dive into Recurrent Neural Networks (RNNs), a powerful class of neural networks designed for processing sequential data, such as time series, text, and speech.
🔍 In this session, we will cover:
Introduction to Recurrent Neural Networks (RNNs):
Understanding the basics and significance of RNNs in deep learning.
Key applications of RNNs in various domains.
RNN Architecture:
-Explaining the structure and components of RNNs.
The concept of hidden states and how information is passed through time steps.
Vanishing and Exploding Gradients:
-Understanding the challenges of training RNNs due to vanishing and exploding gradients.
Techniques to mitigate these issues.
Long Short-Term Memory (LSTM) Networks:
-Introduction to LSTMs and how they overcome the limitations of vanilla RNNs.
Detailed explanation of LSTM cell structure, including input, forget, and output gates.
Practical implementation of LSTM networks using Keras and TensorFlow.
Gated Recurrent Unit (GRU) Networks:
Overview of GRUs as an alternative to LSTMs.
Comparison between GRUs and LSTMs.
Implementation of GRU networks using Keras and TensorFlow.
-Bidirectional RNNs:
Concept of bidirectional RNNs and their advantages in capturing context from both past and future time steps.
Implementation of bidirectional RNNs using Keras and TensorFlow.
Advanced RNN Architectures:
Introduction to more complex RNN architectures, such as stacked RNNs and attention mechanisms.
Hands-On Examples:
Practical demonstrations of building, training, and evaluating RNN models for real-world tasks, such as:
Time series prediction.
Text generation.
Sentiment analysis.
Hyperparameter Tuning and Optimization:
Techniques for tuning and optimizing RNN models for better performance.
Evaluating RNN Models:
Methods for assessing the performance of RNNs using metrics like accuracy, loss, and perplexity.
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