Deep Learning

Deep learning is the branch of machine learning that uses neural networks with many layers to learn directly from raw data such as text, images and audio. It is the technology behind modern language understanding and image recognition, and generative AI tools like ChatGPT and Gemini sit inside it: deep learning is part of machine learning, which is part of AI.

How it works

A neural network passes data through layers of connected units. Each layer transforms its input a little, and together the layers learn increasingly abstract features: edges, then shapes, then objects in an image, or characters, then words, then meaning in text. Training adjusts millions or billions of internal weights through backpropagation so the network’s outputs get closer to the right answers. Unlike classic machine learning, you do not hand-design the features; the network learns them.

Neural networks have existed for decades, but deep learning took off in the early 2010s, once large datasets and GPU computing made it practical to train many layers. Different architectures suit different data: convolutional networks for images, and since 2017 transformers for language, which underpin BERT and GPT models. Deep learning can power both supervised and unsupervised tasks.

How it’s used in SEO and marketing

• Inside search engines: Google’s ranking systems guide lists deep learning systems such as RankBrain, neural matching and BERT.
• Pre-trained models you can use without training anything: NLP APIs, transformer sentiment models, speech-to-text such as Whisper, and vision models that draft alt text.
• Embeddings for clustering keywords and pages by meaning.
• Generative AI for summarising and drafting content.

Example

The MLforSEO sentiment analysis deep dive tests a slang-heavy social post praising an update as “fire”. Two lexicon methods and a classic TF-IDF model all scored it negative, because “fire” looks bad word by word. Only a transformer model trained on millions of tweets read it correctly as positive, which shows what deep learning adds: context.

Limitations and things to watch

• Explainability: the calculations inside a deep model are very hard to turn into a reason a client accepts. MLforSEO’s rule of thumb is to skip deep learning when a stakeholder needs to know exactly why a decision was made.
• It needs large datasets and serious computing power to train, so most marketers should use pre-trained models.
• Outputs can vary between runs, which complicates reporting.
• Models inherit gaps and biases from their training data.

Learn it on MLforSEO

• The Characteristics of a Machine Learning Problem: A Task, Data and Solution Framework for SEOs
• Sentiment Analysis: A Deep Dive Into the Theory, Methods, and Applications
• How to transcribe audio with OpenAI’s Whisper API in Google Colab
• When to use Machine Learning in SEO: Decision Checklist
• Introduction to Machine Learning for SEO (course)