Deep Learning Lecture Notes

Overview

• Deep Learning (DL) is a subset of Machine Learning (ML), which itself is a subset of AI.
• Mimics human brain functionality using Neural Networks (NN).
• Neural Networks consist of interconnected neurons similar to human brain neurons.
• DL is about creating models that learn from data and become more accurate with minimal explicit programming.

Neural Networks

• Input Layer: Receives input data.
• Output Layer: Provides final output of the model.
• Hidden Layers: Layers between input and output, where transformations happen.
• Fully connected networks: Every neuron in one layer is connected to every neuron in the next layer.

Features of Deep Learning

• Capable of identifying features with minimal programmer guidance.
• Good for handling high dimensionality problems by adding more input layers.
• Requires preprocessing steps similar to ML.
• Aims to generalize machine learning models to handle more specific tasks.

Deep Learning Architectures

Types of Neural Networks

1. Deep Neural Networks

   • Multiple hidden layers between input and output.

2. Deep Belief Networks (DBN)

   • Uses contrastive divergence algorithm to learn features.

3. Recurrent Neural Networks (RNN)

   • Supports parallel and sequential computing.
   • Remembers prior information, useful for tasks like speech recognition.

4. Feedforward Neural Networks

   • Information moves in one direction, from input to output.
   • No back loops, used in data compression, pattern recognition.

5. Convolutional Neural Networks (CNN)

   • Specialized for image classification, object recognition.
   • Preferred for high accuracy image processing tasks.

6. Restricted Boltzmann Machines (RBM)

   • Neurons are symmetrically connected.
   • Used in feature learning, anomaly detection.

7. Autoencoders

   • Unsupervised learning for data compression.
   • Encodes inputs to lower dimensions and decodes to reconstruct data.

Applications of Deep Learning

• Face and speech recognition.
• Medical image analysis.
• Self-driving cars, voice control assistants.
• Automatic image caption generation and language translation.

Advantages of Deep Learning

• Reduces need for feature engineering.
• Identifies complex patterns effectively.

Disadvantages and Limitations

• Requires large amounts of data.
• Expensive and time-consuming to train.
• Lacks strong theoretical foundation.
• Learns only through observations and may have bias issues.

Conclusion

• Deep Learning is a powerful tool for AI and ML, offering rich frameworks for solving complex problems.
• Further exploration into algorithms and applications continues to enhance its capabilities.