Deep Learning Overview

Deep Learning is a subset of machine learning inspired by the structure and functioning of the human brain. It uses artificial neural networks with multiple layers to model complex patterns in data. By leveraging vast amounts of data and computational power, deep learning has revolutionized tasks like image recognition, natural language processing, and speech synthesis.

Key Functions of Deep Learning

• Feature Extraction: Automatically identifies significant features from raw data, reducing the need for manual intervention.
• Pattern Recognition: Excels in recognizing complex patterns in high-dimensional data.
• Prediction and Classification: Provides accurate predictions and classifications across domains.
• Data Representation: Captures hierarchical data representations to understand intricate relationships.

Repository Structure

This repository includes projects and resources across key branches of deep learning:

1. Autoencoders

• Use Cases: Dimensionality reduction, anomaly detection, and image denoising.
• Included Projects:
  • Variational Autoencoders (VAEs)
  • Image compression experiments

2. Monkeypox Detection Project

• Models Used:
  • Pre-trained architectures like VGG16 and GoogLeNet.
  • Custom deep learning models for specialized classification.
• Applications: Assists in early detection of monkeypox, enabling timely medical intervention.

3. MNIST Image Classification

• Highlights:
  • Explores fully connected neural networks and CNN architectures.
  • Includes detailed preprocessing and performance evaluation.

4. Face Recognition Image Classification

• Key Features:
  • Explores CNN-based architectures.
  • Includes experiments with transfer learning and custom models.

5. Natural Language Processing (NLP)

• Covered Models:
  • RNN (Recurrent Neural Networks): Sequential data processing.
  • LSTM (Long Short-Term Memory): Handling long-range dependencies in sequences.
  • GRU (Gated Recurrent Units): Efficient and faster sequence processing.
• Applications: Sentiment analysis, text generation, and machine translation.

6. Transfer Learning

• Featured Implementations:
  • Cats vs Dogs Dataset: Binary classification using VGG16.
  • Oxford Flowers 102 Dataset: Multi-class flower classification using GoogLeNet.
  • CIFAR-10 Dataset: General image classification with custom layers added to pre-trained models.

Applications of Deep Learning

• Healthcare: Disease detection, drug discovery, and medical imaging analysis.
• Finance: Fraud detection, risk analysis, and stock market predictions.
• Automotive: Self-driving cars and advanced driver-assistance systems.
• Retail: Recommendation systems and customer behavior analysis.
• Entertainment: Content personalization, voice assistants, and video generation.
• Agriculture: Crop monitoring, yield prediction, and disease detection.