Deep Learning Basics 🧠

Deep learning is a subset of machine learning that involves artificial neural networks with multiple layers. Here's a concise overview:

Key Concepts

• Neural Networks 🧩
  The core of deep learning, consisting of layers (input, hidden, output) that process data.

  

• Activation Functions 📈
  Functions like ReLU, Sigmoid, and Tanh introduce non-linearity.

  

• Training Process 🔄
  Involves forward propagation, loss calculation, and backpropagation.

  

Resources

• Explore Neural Network Architectures 🏗️
• Understanding Gradient Descent 🔄
• Deep Learning Tutorials for Beginners 📘

Tips

• Start with foundational math (linear algebra, calculus) before diving into code.
• Use frameworks like TensorFlow or PyTorch for practical implementation.