Welcome to the world of deep learning! This tutorial will guide you through the basics of neural networks and their applications.
What is Deep Learning?
Deep learning is a subset of machine learning that uses artificial neural networks (ANNs) with multiple layers to model complex patterns. Unlike traditional machine learning, it automatically learns features from raw data.
Key Concepts
- Neural Networks: Mimic the human brain's structure to process information.
- Layers: Input, hidden, and output layers. Hidden layers extract abstract features.
- Activation Functions: Introduce non-linearity (e.g., ReLU, Sigmoid).
Applications of Deep Learning
Deep learning powers technologies like:
- 📸 Image recognition (e.g., CNNs)
- 🎵 Speech processing (e.g., RNNs)
- 🧩 Game playing (e.g., AlphaGo)
- 📈 Financial forecasting
Explore more about machine learning fundamentals here: en/tutorials/machine-learning-basics
Getting Started
- Learn Python basics 📚
- Understand linear algebra and calculus 🧮
- Experiment with frameworks like TensorFlow or PyTorch 🧰
For interactive examples, check out our AI Tutorials section! 🌐