tags : Machine Learning
FAQ
Deep Learning vs Neural Nets
All deep learning involves neural networks, but not all neural networks are “deep” - some have just a single layer.
When we talk about neural networks, we’re discussing a specific approach within the broader field of deep learning.
Neural networks are the fundamental building blocks of deep learning, but the relationship between them is worth clarifying:
- Neural networks are computational models inspired by the human brain’s structure.
- Deep learning refers specifically to neural networks with multiple layers (deep neural networks).
While neural networks are essential to deep learning, there are a few distinctions:
- All deep learning involves neural networks, but not all neural networks are “deep” - some have just a single layer.
- Deep learning specifically refers to neural networks with:
- Multiple hidden layers between input and output
- The ability to automatically learn hierarchical features
- Often more complex architectures (CNNs, RNNs, Transformers, etc.)
It would be technically incorrect to talk about deep learning without neural networks, as neural networks are the defining characteristic of deep learning. Other machine learning approaches like random forests, SVMs, or traditional statistical methods exist outside the deep learning paradigm.
Does that help clarify the relationship between these concepts? I’d be happy to explore specific aspects of neural networks or deep learning in more detail.
Is Deep Leaning Supervised or Unsupervised Learning?
Deep learning can be applied to both supervised and unsupervised learning, as well as other learning paradigms:
Supervised Deep Learning
- Uses labeled data where inputs are mapped to known outputs
- Examples: Image classification (CNNs), sentiment analysis (RNNs/Transformers), object detection (YOLO, R-CNN)
- The network learns to predict the correct labels based on training examples
Unsupervised Deep Learning
- Works with unlabeled data to find patterns or structure
- Examples: Autoencoders, Variational Autoencoders (VAEs), Deep Belief Networks, GANs (in some configurations)
- Often used for dimensionality reduction, feature learning, clustering, or generative modeling
Semi-supervised Deep Learning
- Uses a combination of labeled and unlabeled data
- Particularly useful when labeled data is scarce but unlabeled data is abundant
Self-supervised Deep Learning
- Creates “supervisory signals” from the input data itself without explicit labels
- Examples: BERT, GPT models, contrastive learning approaches, masked language modeling
- Has become increasingly important in recent years
Reinforcement Learning with Deep Networks
- Uses neural networks to learn optimal actions through reward signals
- Examples: Deep Q-Networks (DQN), Proximal Policy Optimization (PPO)