In this article, I will explain What are Transfer Learning Techniques.
Transfer learning is a machine learning technique that involves using knowledge gained while solving one problem to help solve a different but related problem. In the context of deep learning, transfer learning involves using a pre-trained neural network as a starting point for a new, related task. By doing so, the model can leverage the knowledge it has gained from the pre-trained network, allowing it to learn faster and with less data.
Here are some common transfer learning techniques:
- Fine-tuning: This involves taking a pre-trained network and training it on a new dataset for a related task. The weights of the pre-trained network are used as the starting point, and the network is then fine-tuned using the new dataset.
- Feature extraction: This involves using a pre-trained network to extract features from the input data, which can then be used as inputs to a new model. The pre-trained network is frozen, and the new model is trained on the extracted features.
- Multi-task learning: This involves training a neural network to perform multiple related tasks simultaneously. By doing so, the network can learn to share knowledge between the tasks and improve performance on all of them.
Transfer learning can be particularly useful when working with limited amounts of data or when training a deep neural network from scratch is not feasible due to computational constraints. By leveraging the knowledge gained from pre-trained networks, transfer learning can help reduce the amount of data needed to train a model and improve its performance on the target task.
- Dot Net Framework
- Power Bi
- Scratch 3.0