META-LEARNING.pptx
- 1. META-LEARNING Meta-learning, also known as “learning to learn”, is a subset of machine learning in computer science.
- 2. Agenda • What is Meta Learning? • Why is Meta Learning is important? • What is the interest in meta learning? • How does meta learning work? • Benefits • Reference
- 3. Meta-Learning • The performance of a learning model depends on its training dataset, the algorithm, and the parameters of the algorithm. • Many experiments are required to find the best-performing algorithm and parameters of the algorithm. • Meta-learning approaches help find these and optimize the number of experiments. This results in better predictions in a shorter time. • Meta learning can be used for different machine learning models (e.g. few-shot learning, reinforcement learning, natural language processing, etc.) • Meta-learning algorithms make predictions by taking the outputs and metadata of machine-learning algorithms as input. • Meta-learning algorithms can learn to use the best predictions from machine-learning algorithms to make better predictions.
- 4. What is Meta Learning? Meta-learning, also known as “learning to learn”, is a subset of machine learning in computer science. It is used to improve the results and performance of a learning algorithm by changing some aspects of the learning algorithm based on experiment results. Meta-learning helps researchers understand which algorithm(s) generate the best/better predictions from datasets. Meta-learning algorithms use metadata of learning algorithms as input. Then, they make predictions and provide information about the performance of these learning algorithms as output. For non-technical users, metadata is data about data. For example, the metadata of an image in a learning model can be its size, resolution, style, date created, and owner. Systemic experiment design in meta-learning is the most important challenge.
- 5. Why is Meta Learning is important? Machine learning algorithms have some challenges such as • Need for large datasets for training • High operational costs due to many trials/experiments during the training phase • Experiments/trials take a long time to find the best model which performs the best for a certain dataset. • Meta learning can help machine learning algorithms to tackle these challenges by optimizing learning algorithms and finding learning algorithms that perform better.
- 6. What is the interest in meta learning? The interest in meta-learning has been growing during the last five years, it has especially accelerated after 2017. As the use of deep learning and advanced machine learning algorithms has increased, the difficulties to train these algorithms have increased interest in meta- learning studies.
- 7. How does meta learning work? In general, a meta learning algorithm is trained with outputs (i.e. the model’s predictions) and metadata of machine learning algorithms. After training, its skills are tested and used to make final predictions. Meta learning covers tasks such as- • observing the performance of different machine learning models about learning tasks • learning from meta data • performing faster learning process for new tasks
- 8. EXAMPLE We may want to train a model to label different breeds of dogs. • We first need an annotated data set • Various ML models are built on the training set. They could focus just on certain parts of the dataset • The meta training process is used to improve the performance of these models • Finally, the meta training model can be used to build a new model from a few examples based on its experience with the previous training process
- 9. Benefits Meta learning algorithms are used to improve machine learning solutions. The benefits of meta learning are- • Higher model prediction accuracy • A faster, cheaper training process • Building more generalized models
- 10. Reference • Dilmegani, C. (2022). What is Meta Learning? Techniques, Benefits & Examples . Retrieved from AIMultiple: https://research.aimultiple.com/meta-learning/#what-are-the- benefits-of-meta-learning
- 11. Thank You