Deep Learning Basics

Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.

Key Concepts

• Neural Networks: Inspired by the human brain, neural networks are composed of interconnected nodes or "neurons" that work together to process information.
• Layers: A deep learning model consists of multiple layers, including input, hidden, and output layers.
• Activation Functions: These functions help determine whether a neuron should be activated or not based on the input it receives.

Getting Started

To dive deeper into deep learning, you can explore our comprehensive tutorial series on Deep Learning Fundamentals.

Useful Resources

• TensorFlow - An open-source library for machine learning and deep learning.
• Keras - A high-level neural networks API, written in Python and capable of running on top of TensorFlow.

Neural Network Diagram

By understanding the basics of deep learning, you'll be well on your way to exploring more advanced topics and building your own models. Happy learning! 🎓