Deep Learning Basics 🧠

Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) to model complex patterns in data. Here's a quick overview of key concepts:

1. Neural Network Structure
   A neural network consists of layers:

   • Input layer (receives data)
   • Hidden layers (processes data)
   • Output layer (produces predictions)
   

2. Training Process

   • Forward propagation: Data flows through the network
   • Backpropagation: Adjusts weights using gradients
   • Optimization: Minimizes loss with algorithms like SGD
   

3. Popular Frameworks

   • TensorFlow (Google)
   • PyTorch (Facebook)
   • Keras (user-friendly API)
   

For hands-on practice, check out our TensorFlow Tutorial or PyTorch Examples section. 🚀