Deep Learning
A branch of machine learning that learns complex, non-linear relationships in data through multi-layered artificial neural networks. It automatically extracts features from data and serves as a core technology in fields requiring high cognitive abilities, such as image recognition, natural language processing, and generative AI.
Detailed explanation
Why It Matters in Tool Selection
You should decide whether to adopt deep learning based on the complexity of the problem you want to solve. While traditional machine learning might be more efficient for simple predictions based on structured data, deep learning-based tools are essential for analyzing images, voice, unstructured text, or generating creative content. In addition, deeper models incur higher costs for high-performance hardware, making it a key criterion for balancing budget and performance.
What to Check
- Is the problem you are trying to solve based on unstructured data such as images or language?
- Do you have the necessary GPU/NPU resources secured for model training or inference?
- Is a large-scale, high-quality dataset prepared for training?
- Is real-time response (low latency) critical, or is accuracy the top priority?
Examples
A smartphone's Face ID recognizes a user's facial features through deep learning, and ChatGPT generates human-like sentences using a deep learning model with billions of parameters.
Confusing Terms
Machine Learning
A broader concept that includes deep learning, encompassing algorithms that manually extract features.
Artificial Neural Networks (ANN)
Refers to the underlying structure itself that forms the foundation of deep learning; deep learning is when these structures are stacked in multiple layers.