Welcome to the first course in our deep learning series! In this tutorial, we will cover the basics of deep learning, including its history, key concepts, and applications.
What is Deep Learning?
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 process information.
- Layers: Deep learning models consist of multiple layers, including input, hidden, and output layers.
- Activation Functions: These functions help determine whether a neuron should be activated or not.
- Backpropagation: This is the process of adjusting the weights of the neurons based on the error of the output.
Applications of Deep Learning
Deep learning has revolutionized various fields, including:
- Image Recognition: Identifying objects, faces, and scenes in images.
- Natural Language Processing (NLP): Understanding and generating human language.
- Medical Diagnosis: Analyzing medical images to detect diseases.
- Autonomous Vehicles: Enabling cars to navigate and make decisions on the road.
Resources
For further reading, check out our Deep Learning Tutorial Series.
Deep Learning Architecture