This repository contains comprehensive implementations and experiments in deep learning, showcasing cutting-edge architectures and frameworks.

• Neural Network (NN)

  • Foundational architecture for deep learning
  • Basic building blocks and concepts

• Deep Neural Network (DNN)

  • Multi-layer architectures
  • Advanced activation functions
  • Dropout and regularization techniques

• Convolutional Neural Networks (CNNs)

  • Image processing and computer vision
  • Feature extraction and classification
  • State-of-the-art architectures (ResNet, VGG, Inception)

• Recurrent Neural Networks (RNNs)

  • Sequence modeling
  • Time series analysis
  • Natural language processing tasks

• Long Short-Term Memory (LSTM)

  • Advanced sequence modeling
  • Memory cell architecture
  • Gradient flow optimization

• Gated Recurrent Units (GRUs)

  • Simplified recurrent architecture
  • Efficient training and inference
  • Performance optimization

• Model implementation
• Training pipelines
• Deployment strategies

• High-level neural network API
• Rapid prototyping
• Model experimentation

git clone https://github.com/A-A7med-i/DL.git
cd deep_learning_repo

Contributions are welcome! Please read the contributing guidelines for more information.