Was ist Transfer Learning?

Transfer learning is a machine learning technique in which a model trained on a specific task is applied to another, similar task. The already trained model is used as a starting point to learn more quickly and efficiently on a new task by transferring the patterns and features that it learned in the first task to the new task.

This can be particularly useful when there is not enough training data to train a model from scratch for the new task. By using transfer learning, an already trained model can serve as a basis and be adapted to the new task by only requiring a small amount of training data.

Transfer learning is used in various application areas such as image and language processing, but also in robotics and medicine.

What is transfer learning not suitable for?

Those : topbots.com

Transfer learning can be an effective technique for applying a trained model to a new similar task, but there are also some situations where it is not suitable:

• If the new task is too different from the original task, the transferred knowledge may not be useful and the model will need to be retrained from scratch.
• If the data from the new task is not sufficiently similar to the data from the original task, the transferred knowledge may not be useful and the model will need to be retrained.
• If the original model was trained on a task that requires specific domain knowledge, the transferred knowledge may not be useful if the new task requires different domain knowledge.

In these cases, transfer learning may be ineffective and it may be better to train the model from scratch or use alternative machine learning techniques.

Where is transfer learning not used?

Those : analyticssteps.com

Transfer learning can fundamentally be used in many application areas of artificial intelligence to improve the efficiency and effectiveness of models. However, there are certain use cases where transfer learning may not be the best option. For example, it may be less effective on tasks that require very specific characteristics. Also, transfer learning may not be appropriate for use cases where limited data is available or the data varies widely. In such cases, other approaches such as Unsupervised Learning or Reinforcement Learning be more suitable.

What is the difference between transfer learning and deep learning?

Those : datawow.io

Transfer Learning und Deep Learning are not competing approaches but can be used together. Deep learning is a branch of machine learning that uses multi-layer neural networks to recognize and learn complex patterns in data. Transfer learning is a method that allows already trained models to be applied to new similar tasks by transferring the knowledge and skills of the already trained model to the new task.

In concrete terms, this means that with transfer learning, an already trained deep learning model is used as a starting point in order to adapt it to a new, similar task. The model is not trained from scratch, but is simply adapted to new data. This can mean that a model can be trained faster and requires less data than a new model.