What is Deep Learning?
Deep Learning is a subset of machine learning that uses artificial neural networks with multiple layers (deep neural networks) to progressively extract higher-level features from raw input data, enabling automatic learning of representations for tasks such as classification, detection, and generation.
Quick Facts
| Created | 2006 by Geoffrey Hinton et al. |
|---|---|
| Specification | Official Specification |
How It Works
Deep learning architectures consist of multiple processing layers that transform input data through a series of nonlinear operations. Each layer learns increasingly abstract representations of the data. The term 'deep' refers to the number of layers through which data is transformed. Unlike traditional machine learning algorithms that require manual feature engineering, deep learning automatically discovers the representations needed for feature detection or classification from raw data. This approach has achieved breakthrough results in computer vision, natural language processing, speech recognition, and many other domains. The concept of foundation models has emerged, referring to large-scale models trained on broad data that can be adapted to many downstream tasks. These include language models like GPT and BERT, vision models like CLIP and SAM, and multimodal models that combine multiple modalities.
Key Characteristics
- Hierarchical feature learning through multiple neural network layers
- Automatic feature extraction without manual engineering
- Requires large amounts of training data for optimal performance
- Computationally intensive, often requiring GPU acceleration
- Capable of learning complex nonlinear relationships in data
- End-to-end learning from raw inputs to final outputs
Common Use Cases
- Image recognition and object detection in computer vision
- Natural language processing including translation and text generation
- Speech recognition and synthesis systems
- Autonomous vehicles and robotics perception
- Medical diagnosis from imaging data (X-rays, MRI, CT scans)
Example
Loading code...Frequently Asked Questions
What is deep learning in simple terms?
Deep learning is a type of machine learning that uses artificial neural networks with multiple layers to automatically learn patterns from data. Unlike traditional ML that requires manual feature engineering, deep learning discovers representations needed for tasks like image recognition and language understanding directly from raw data.
What is the difference between AI, machine learning, and deep learning?
AI is the broadest concept - machines mimicking human intelligence. Machine learning is a subset of AI where systems learn from data. Deep learning is a subset of ML using multi-layer neural networks. Deep learning is the most specialized, achieving breakthrough results in vision and language tasks.
Why is deep learning called 'deep'?
The term 'deep' refers to the number of layers in the neural network. Deep networks have many hidden layers (dozens to hundreds) that progressively extract higher-level features from raw input data, enabling learning of complex hierarchical representations.
What are common deep learning architectures?
Common architectures include CNNs (Convolutional Neural Networks) for images, RNNs/LSTMs for sequences, Transformers for language and vision, GANs for generation, and Autoencoders for representation learning. Foundation models like GPT and BERT combine multiple techniques.
What hardware is needed for deep learning?
Deep learning is computationally intensive and typically requires GPU acceleration. NVIDIA GPUs with CUDA are most common. Cloud platforms (AWS, GCP, Azure) offer GPU instances. For training large models, multiple GPUs or TPUs may be needed. Inference can often run on CPUs or edge devices.