Deep Learning - README

This document provides a simple overview of Deep Learning, covering the main architectures:

• Artificial Neural Networks (ANNs)
• Convolutional Neural Networks (CNNs)
• Transfer Learning
• Sequence Learning (RNN, LSTM, GRU)

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1. Artificial Neural Network (ANN)

ANN is the foundation of deep learning, inspired by the way neurons work in the human brain.

Key Concepts

• Perceptron: Basic unit (like a single neuron) that takes inputs, applies weights, sums them, and passes the result through an activation function.
• Layers:
  • Input Layer: Takes in the data.
  • Hidden Layers: Process the data and extract patterns.
  • Output Layer: Produces the final result.
• Activation Functions: Add non-linearity to help the model learn complex patterns.
  • Examples: ReLU, Sigmoid, Tanh.
• Loss Functions: Measure how wrong the predictions are.
  • Examples: Mean Squared Error, Cross-Entropy.
• Optimization: Adjusts weights to reduce loss.
  • Example: Gradient Descent, Adam, RMSProp.

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2. Convolutional Neural Network (CNN)

CNNs are specialized for images and visual data. They use filters to detect features like edges, textures, and shapes.

Key Concepts

• Convolution Layer: Uses filters (kernels) to extract features from the image.
• Pooling Layer: Reduces the size of feature maps (downsampling), making the network faster and reducing overfitting.
• Flattening: Converts 2D feature maps into 1D for fully connected layers.
• Fully Connected Layer: Combines features and makes final predictions.
• Applications: Image classification, object detection, face recognition, medical imaging.

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3. Transfer Learning

Transfer learning allows us to use models trained on large datasets and adapt them to new problems.

Key Concepts

• Pre-trained Models: Models like VGG, ResNet, Inception trained on ImageNet.
• Feature Extraction: Using pre-trained model layers to extract useful features.
• Fine-Tuning: Retraining some layers with your own dataset for better performance.
• Applications: Helpful when your dataset is small or training from scratch is expensive.

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4. Sequence Learning

Sequence models handle data that comes in order (time, text, speech, etc.).

Types

• RNN (Recurrent Neural Network):

  • Remembers past information.
  • Good for short sequences, but struggles with long-term memory (vanishing gradient problem).

• LSTM (Long Short-Term Memory):

  • Improved RNN with gates (input, forget, output).
  • Captures long-term dependencies.
  • Widely used in text and speech tasks.

• GRU (Gated Recurrent Unit):

  • Similar to LSTM but simpler (only update & reset gates).
  • Faster to train while still effective for long sequences.

Applications

• Natural Language Processing (NLP)
• Speech Recognition
• Machine Translation
• Sentiment Analysis
• Time-Series Prediction

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Conclusion

• ANN → General-purpose networks.
• CNN → Best for image and spatial data.
• Transfer Learning → Saves time using pre-trained models.
• Sequence Learning (RNN, LSTM, GRU) → Best for sequential or time-dependent data.

Deep Learning = Choosing the right architecture for the right data

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📩 Contact

👤 Muhammad Sayyad Khan

📧 msswati43215@gmail.come

🔗 [https://www.linkedin.com/in/sayyad-khan-16250a377?utm_source=share&utm_campaign=share_via&utm_content=profile&utm_medium=android_app]

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