![]() ![]() Recurrent neural networks (RNN) are a class/type of artificial neural networks, and they are applied to different machine learning problems, such as problems that have sequential data or time series data. MAML is a general optimization and task-agnostic algorithm, and it is used to train the parameters of a model for fast learning with a small number of gradient updates. The neural network is trained by using a few examples to adapt the model to new tasks faster. This model gives good results in few-shot classification tasks. Metric learning means learning a metric space for predictions. We briefly explained some common approaches and methods in meta learning domain below. There are different approaches in meta learning as model-based, metrics-based, and optimization-based approaches. Meta learning is used in various areas of the machine learning domain. SOURCE:KDNUGGETS What are the approaches and applications in meta learning? 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.The meta training process is used to improve the performance of these models.They could focus just on certain parts of the dataset Various ML models are built on the training set.performing faster learning processes for new tasksįor example, we may want to train a model to label different breeds of dogs.observing the performance of different machine learning models about learning tasks.After training, its skills are tested and used to make final predictions. the model’s predictions) and metadata of machine learning algorithms. In general, a meta learning algorithm is trained with outputs (i.e. Source: Google Trends How does meta learning work? As use of deep learning and advanced machine learning algorithms has increased, the difficulties in training these learning algorithms have created an increase in interest for meta learning studies. The interest in meta learning has been growing during the last five years, it has especially accelerated after 2017. Meta learning can help machine learning algorithms to tackle these challenges by optimizing learning algorithms and finding learning algorithms that perform better. Experiments/trials take a long time to find the best model which performs the best for a certain dataset.High operational costs due to many trials/experiments during the training phase.Machine learning algorithms have some challenges, such as ![]() Systemic experiment design in meta learning is the most important challenge. ![]() For example, the metadata of an image in a learning model can be its size, resolution, style, date created, and owner. For non-technical users, metadata is data about data. Then, they make predictions and provide information about the performance of these learning algorithms as output. Meta learning algorithms use metadata of learning algorithms as input. Meta learning helps researchers understand which algorithm(s) generate the best/better predictions from datasets. 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, also known as “learning to learn”, is a subset of machine learning in computer science. In computer science, meta learning studies and approaches started in the 1980s and became popular after Jürgen Schmidhuber and Yoshua Bengio‘s works on the topic. Meta learning algorithms can learn to use the best predictions from machine-learning algorithms to make better predictions. Meta learning algorithms make predictions by taking the outputs and metadata of machine-learning algorithms as input. Meta learning can be used for different machine learning models (e.g., few-shot learning, reinforcement learning, natural language processing, etc.). This results in better predictions in a shorter time. Meta-learning approaches help find these and optimize the number of experiments. Many experiments are required to find the best-performing algorithm and parameters of the algorithm. The performance of a learning model depends on its training dataset, the algorithm, and the parameters of the algorithm. ![]()
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