Welcome to the Deep Learning Fundamentals guide! This tutorial covers the core concepts of deep learning, including neural networks, activation functions, and training processes.
What is Deep Learning?
Deep learning is a subset of machine learning that uses neural networks with multiple layers to model complex patterns. Unlike traditional machine learning, it automatically learns features from raw data.
- Key Components:
- Neural network architecture 🧠
- Activation functions (ReLU, Sigmoid, etc.) 📈
- Loss functions and optimization 🔄
Getting Started with Neural Networks
A neural network consists of layers:
- Input Layer: Receives raw data.
- Hidden Layers: Process data through weights and biases.
- Output Layer: Produces the final result.
Training Process
Training involves adjusting weights to minimize error:
- Forward propagation 🔁
- Backward propagation 📉
- Gradient descent 🔁
For a deeper dive into neural network architectures, check out our Neural Network Architecture Tutorial.
Common Activation Functions
- ReLU (Rectified Linear Unit) 📈
- Sigmoid (Logistic Function) 📊
- Tanh (Hyperbolic Tangent) 📈
Explore more about activation functions and their applications.
Note: This content is for educational purposes only. For advanced topics, refer to our Deep Learning Resources.