Definition: Deep learning is a subset of machine learning that uses artificial neural networks with multiple layers to learn complex patterns from data.

Detailed Description:

Deep learning models are inspired by the structure of the human brain and are capable of learning complex features from raw data. They have achieved significant breakthroughs in various fields, including:

• Image recognition: Identifying objects, faces, and scenes within images.
• Natural language processing: Understanding and generating human language.
• Speech recognition: Transcribing spoken language into text.
• Autonomous vehicles: Perceiving the environment and making decisions.

Deep learning techniques often involve large datasets and powerful computing resources.

Related Terms:

• Machine Learning
• Neural Networks
• Artificial Neural Networks
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs)

FAQs:

1. What is the difference between deep learning and machine learning?

   • Deep learning is a specific type of machine learning that uses neural networks with multiple layers.

2. Why is deep learning so effective?

   • Deep learning models can learn complex patterns and features from data, making them suitable for tasks that are difficult for traditional machine learning algorithms.

3. What are some challenges in deep learning?

   • Challenges include the need for large datasets, computational resources, and careful hyperparameter tuning.