Deep Learning
Machine learning using neural networks with multiple layers. Enables automatic feature learning and powers modern AI breakthroughs in vision, language, and more.
In-Depth Explanation
Deep learning uses multi-layered neural networks to learn hierarchical representations from data. It's the technology behind most modern AI breakthroughs.
What makes learning "deep":
- Multiple hidden layers (hence "deep")
- Automatic feature learning from raw data
- Hierarchical representations (simple to complex)
- End-to-end training with backpropagation
Deep learning advantages:
- No manual feature engineering
- Scales with data and compute
- Learns complex patterns
- Transfers across tasks
Key architectures:
- CNNs: Computer vision
- RNNs/LSTMs: Sequential data (now mostly transformers)
- Transformers: Language, vision, multimodal
- GANs: Generative models
- Autoencoders: Representation learning
Requirements:
- Large datasets (usually)
- Significant compute (GPUs/TPUs)
- Expertise to design and train
- Or... use pre-trained models!
Business Context
Deep learning powers the AI capabilities businesses use today - from image recognition to chatbots. Most often accessed via APIs and pre-trained models rather than training from scratch.
How Clever Ops Uses This
We leverage deep learning through foundation models and fine-tuning, making advanced AI accessible to Australian businesses without massive data science teams.
Example Use Case
"A deep learning model trained on millions of images learning to recognise visual patterns, from edges in early layers to complex objects in later layers."