Notes on Deep learning

1. B.Tech Specialization in Artificial Intelligence and Data Science
Semester: 5th
Deep Learning
BTAI-25-508
Unit : $
Jyoti Bala
E.Code :
E24T6554
FACULTY OF ENGINEERING TECHNOLOGY & COMPUTING

2. Recurrent Networks:
An Overview
Recurrent Neural Networks (RNNs) are a class of artificial neural networks
designed to process sequential data. Unlike traditional neural networks,
RNNs utilise an internal memory to process sequences of inputs, making
them ideal for tasks in language, speech, and time-series analysis. Their
unique architecture allows them to handle variable-length inputs via
recurrent connections, forming the foundation for many advanced sequence
models we rely on today.

3. Traditional Recurrent Neural Networks (RNNs)
Sequential Processing
RNNs process data points in a sequence, with each step's
output depending on previous computations.
Internal Memory
They maintain a 'hidden state' that acts as a memory,
capturing information from prior steps.
Parameter Sharing
Weights and biases are shared across all time steps,
enabling learning of sequential patterns.
Gradient Challenges
Prone to vanishing/exploding gradients, hindering learning
of long-term dependencies.
The hidden state is updated as (a^{langle t rangle} = g(W_{aa}a^{langle t-1 rangle} + W_{ax}x^{langle t rangle} + b_a)), and the
output is (y^{langle t rangle} = g(W_{ya}a^{langle t rangle} + b_y)).
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4. Bidirectional Recurrent Neural Networks (BRNNs)
BRNNs enhance traditional RNNs by processing sequences in both forward and backward
directions simultaneously. This dual processing allows the network to capture context from
both past and future time steps for each point in the sequence, offering a more
comprehensive understanding.
The architecture typically involves two distinct RNN layers: one that reads the input
sequence from left to right, and another that reads it from right to left.
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5. Architectural Foundations
Encoder-Decoder Systems
Encode Input
Generate Context
Decode Output
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6. Sequence-to-Sequence (Seq2Seq) Models
Enhanced Encoder-Decoder
Seq2Seq models are an extension of
the encoder-decoder framework,
typically implemented using RNNs.
They are designed to handle
complex many-to-many sequence
transformations, where both input
and output can be of variable
lengths.
Attention Mechanisms
A key innovation in Seq2Seq models
is the incorporation of attention
mechanisms. This allows the
decoder to selectively focus on
different parts of the input sequence
during output generation, significantly
improving context retention and
overall performance.
Versatile Applications
Seq2Seq models power a wide array
of applications, including advanced
machine translation systems,
intelligent chatbots, and
sophisticated speech synthesis,
demonstrating their versatility in
complex sequential tasks.
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7. Deep Recurrent Neural Networks
• Hierarchical Feature Learning: Deep RNNs stack multiple recurrent
layers on top of each other, allowing the network to learn features at
various levels of abstraction. Lower layers capture basic patterns, while
higher layers learn more complex representations.
• Increased Capacity: This hierarchical structure significantly improves the
model's capacity and representation power, enabling it to handle more
intricate sequence modeling tasks.
• Training Considerations: Training Deep RNNs requires careful
techniques such as gradient clipping to manage exploding gradients and
robust regularisation methods to prevent overfitting.
• Complex Applications: They are particularly effective in complex
sequence modeling scenarios, such as detailed video analysis, where
deep understanding of temporal dependencies is crucial.
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8. Autoencoders with Recurrent Networks
Encoder RNN
The encoder RNN processes the input
sequence, compressing it into a
compact latent vector, which captures
the essential features of the sequence.
Latent Vector
This compressed representation serves
as a bottleneck, forcing the model to
learn the most significant information.
Decoder RNN
The decoder RNN takes the latent
vector and reconstructs the original
sequence from it, aiming for an output
as close to the input as possible.
Feature Learning
This process enables the autoencoder
to learn robust and compressed
representations of sequences.
RNN Autoencoders are widely used in applications like anomaly detection in time series data and generating new sequences that
mimic the learned patterns.
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9. Mastering RNN Training
Techniques & Challenges
Backpropagation Through Time
Gradient Clipping
LSTM/GRU Units
Large Datasets & Regularization

10. Applications of Recurrent Networks
• Natural Language Processing (NLP): Fundamental for machine translation, sentiment analysis, and text summarization.
• Speech Recognition and Synthesis: Powers voice assistants and converts text to speech.
• Time Series Forecasting: Used in predicting stock prices, weather patterns, and energy consumption.
• Video and Music Generation: Enables creation of realistic video sequences and composing musical pieces.
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11. Summary & Future Directions
Recurrent Neural Networks are foundational for modeling sequential data, with advanced variants like LSTM and GRU effectively
addressing historical limitations. Encoder-decoder and Sequence-to-Sequence architectures have enabled complex tasks such
as machine translation and chatbots. The future of recurrent networks lies in their seamless integration with attention
mechanisms and the broader Transformer architecture, promising even more powerful and efficient models for sequence
processing across various domains.
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12. Thank You
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For Any Query Contact :
Er. Jyoti Bala
ap4.cse@deshbhagatuniversity.in