Standard 7 stops to get here · leads to 1

Catastrophic Forgetting

The tendency of neural networks to completely forget previously learned information when learning new tasks.

Your route here

7 stops · basics first
  1. Dataset ✓ understood

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

  2. Training ✓ understood

    The process of fitting a model to data by repeatedly measuring how wrong its outputs are and adjusting its parameters to reduce that error.

  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. Unsupervised Learning ✓ understood

    Learning from unlabeled data to discover hidden patterns, structures, or relationships without explicit target outputs.

  5. Self-Supervised Learning ✓ understood

    Learning representations from unlabeled data by creating supervised tasks from the data itself (masked prediction, contrastive learning).

  6. Pre-training ✓ understood

    Training a model on a large dataset (often self-supervised) before fine-tuning on specific tasks, enabling transfer learning.

  7. Fine-Tuning ✓ understood

    The process of further training a pre-trained model on a specific dataset to adapt it for a particular task or domain.

  8. Catastrophic Forgetting · you are here ✓ understood

Where it sits

Before this

Fine-Tuning
Catastrophic Forgetting

Leads to

Continual Learning

Explore nearby

Training Continual Learning Learning new tasks sequentially without forgetting previously learned tasks, addressing catastrophic forgetting. Training Transfer Learning Leveraging knowledge learned from one task/domain to improve performance on a related task with less data. Training LoRA Low-Rank Adaptation - a parameter-efficient fine-tuning method that updates only small low-rank matrices instead of full weights. Training Multi-Task Learning Training a single model on multiple related tasks simultaneously to improve generalization and efficiency. Training Online Learning Models that learn continuously from streaming data, updating incrementally as new data arrives.