Standard 6 stops to get here

Multimodal Learning

Training models on multiple types of data (text, images, audio) to understand relationships across modalities.

Your route here

6 stops · basics first
  1. Dataset ✓ understood

    A collection of data examples used for training, validating, or testing machine learning models.

  2. Feature ✓ understood

    A single measurable property of an example, such as a house's floor area or how many links an email contains, used as an input to a model.

  3. Machine Learning ✓ understood

    Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.

  4. Neural Network ✓ understood

    A computational model inspired by biological neural networks, consisting of interconnected nodes (neurons) organized in layers that process information through weighted connections.

  5. Deep Learning ✓ understood

    A subset of machine learning that uses neural networks with multiple layers (deep neural networks) to learn hierarchical representations of data.

  6. Representation Learning ✓ understood

    Learning useful features or representations of data automatically, rather than hand-crafting them.

  7. Multimodal Learning · you are here ✓ understood

Where it sits

Before this

Representation Learning
Multimodal Learning

Leads to

Nothing yet: a destination in its own right.

Explore nearby

Vision & Multimodal Multimodal Model Models processing multiple data types (text, images, audio) jointly, like GPT-4V, Gemini, or CLIP. Vision & Multimodal CLIP Contrastive Language-Image Pre-training - a model jointly trained on images and text, enabling zero-shot image classification. Training Contrastive Learning A self-supervised learning approach that learns representations by contrasting similar and dissimilar examples. Vision & Multimodal Vision Transformer Applying the transformer architecture to computer vision by treating image patches as tokens, achieving state-of-the-art results. Language & LLMs Cross-Attention Attention between two different sequences, where queries come from one and keys/values from another.