Deep Learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn from large amounts of data.
What is Deep Learning?
Deep Learning is inspired by the human brain and how it processes information. It involves layers of interconnected nodes, each representing a feature of the data. These layers work together to extract increasingly complex features from the raw data.
Key Components of Deep Learning
- Neural Networks: The fundamental building blocks of deep learning.
- Layers: Each layer extracts features from the data.
- Activation Functions: Determine whether a neuron should be activated or not.
- Backpropagation: An algorithm that helps adjust the weights of the neurons to improve the accuracy of the model.
Applications of Deep Learning
Deep Learning has found applications in various fields, including:
- Image Recognition: Identifying objects, faces, and scenes in images.
- Speech Recognition: Converting spoken words into text.
- Natural Language Processing: Understanding and generating human language.
- Medical Imaging: Diagnosing diseases from medical images.
Resources
For further reading on Deep Learning, you can explore our Deep Learning Course.
Deep Learning Architecture