![]() The group of predetermined parameters is searched for the best performing one. Grid Search: This method makes use of manually predetermined hyperparameters. We shall give a simple definition of a couple of methods and cover them in detail in a future article. There exist many ways to optimize hyperparameters. Such a system has become too complicated for humans to optimize fully. Yet, deep neural networks have hundreds of hyperparameters. Human engineers can optimize a few parameters for configuration. The complexity of models makes them increasingly difficult to configure. However, with the ever-increasing complexity of models, more so neural networks, a challenge arises. It means that the process of choosing hyperparameters dramatically affects how well an algorithm learns. We mentioned that hyperparameters have a great impact on the training process. An example of a hyperparameter is the number of branches in a decision tree.Ī good number of machine learning models have many hyperparameters that are optimizable. Hyperparameters have a direct impact on the quality of the training process. It is a parameter that is defined before the start of the learning process. Optimized meta-learningĪ hyperparameter is a parameter whose value is used to control the learning process. We cover three approaches in this section. Various literature details different kinds of approaches to meta-learning. This helps the development of a hypothesis for a new task. In the context of meta-learning, it is made up of knowledge of prior learning experiences. These aspects include the representation of the hypothesis or parameters.Įxtracting insights from Metadata: Metadata is a set of data that describes other data. Essential aspects of the learner can be changed to achieve a dynamic inductive bias. This simply means that the inductive bias of a learner is altered to match a given task. Dynamic bias induction refers to where bias is constructed as a function of the learning task. This is when the algorithm is given inputs it has never come across. To carry out meta-learning, we need such a learning algorithm.ĭynamic inductive bias: Inductive bias is the set of assumptions a learning algorithm uses to make predictions. The learner is used to learn the optimal parameters as well as algorithms for a given task. Inclusion of a learning algorithm: A learning algorithm is critical in the process of “learning how to learn”. We shall better understand meta-learning in the later sections of this article by exploring this technique’s approaches. It may also change an algorithm’s learning rules by altering how the algorithm searches the hypothesis space. This might be through the tuning of hyperparameters or the selection of features. Meta-learning impacts the hypothesis space for learning algorithms. It seeks to apply machine learning to learn the most suitable parameters and algorithms for a given task.Ī hypothesis space may be defined as a set of all hypotheses that may be returned by a machine learning model. Taking inspiration from how human beings learn, meta-learning attempts to automate traditional machine learning challenges. We learn very quickly and efficiently from a handful of examples. ![]() Human beings do not need a large pool of examples to know. ![]() It contrasts with how humans take in new information and learn new skills. Traditional machine learning has us use a sizeable dataset exclusive to a given task to train a model. Metadata is data that describes other data. It is a learning process that applies to understand algorithms to metadata. Very simply defined, meta-learning means learning to learn. To fully appreciate this article, I recommend having a grasp on the basic concepts of machine learning and deep learning. They implement meta-learning to achieve this automation. Machine learning may be used to learn the most suitable parameters and algorithms for a given task to automate this process. This may be based on their experience, biases, or assumptions. For instance, they need to assess algorithms and tune many parameters. The application of machine learning to real-world problems is a very involving task for data scientists. ![]()
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