Training: Difference between revisions
(Created page with "{{see also|Machine learning terms}} ==Introduction== Machine learning is a branch of artificial intelligence that seeks to develop algorithms and statistical models that enable computers to perform tasks without explicit programming. Training plays an integral role in this process, as it enables the algorithm to learn from data and make predictions based on patterns it detects. In this article, we'll examine training in machine learning in depth - its purpose, different...") |
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{{see also|Machine learning terms}} | {{see also|Machine learning terms}} | ||
==Introduction== | ==Introduction== | ||
In [[machine learning]], [[training]] enables the [[model]] to learn from [[data]]. The goal of training a model is to determine the optimal [[parameters]] ([[weights]] and [[biases]]) for the model so it can [[accurate]]ly make predictions when presented with new data. | |||
==Purpose of Training== | ==Purpose of Training== | ||
Training | Training involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen data. Training allows the model to generalize from what it has seen so that it can accurately make predictions even when presented with unknown variables. | ||
==Types of Training== | ==Types of Training== | ||
Machine learning employs various types of training, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. | Machine learning employs various types of training, such as [[supervised learning]], [[unsupervised learning]], [[semi-supervised learning]] and [[reinforcement learning]]. | ||
===Supervised Learning== | ===Supervised Learning=== | ||
Supervised learning is the most common type of machine learning training. This method involves providing the algorithm with a labeled dataset, in which each data point has an assigned class label. The algorithm then utilizes this labeled data to learn relationships between features in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen data sets. | [[Supervised learning]] is the most common type of machine learning training. This method involves providing the [[algorithm]] with a [[labeled dataset]], in which each [[data point]] has an assigned [[class]] [[label]]. The algorithm then utilizes this labeled data to learn relationships between [[features]] in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen [[data sets]]. | ||
===Unsupervised Learning== | ===Unsupervised Learning=== | ||
Unsupervised learning is a type of machine-learning training in which an algorithm is given data that has not yet been labeled. The objective is for this | [[Unsupervised learning]] is a type of machine-learning training in which an algorithm is given data that has not yet been labeled. The objective is for this algorithm to discover patterns and relationships without the assistance of labeled information. Unsupervised learning is often employed for [[clustering]] and [[dimensionality reduction]] tasks, as it allows the computer to recognize structure within data that may otherwise go undetected. | ||
===Semi-Supervised Learning== | ===Semi-Supervised Learning=== | ||
Semi-supervised learning is a type of training that incorporates elements from both supervised and unsupervised learning methods. In this type of scenario, an algorithm receives some labeled data but most of it remains unlabeled. The algorithm then utilizes this labeled information to direct its learning process and make predictions about unlabeled data. | Semi-supervised learning is a type of training that incorporates elements from both supervised and unsupervised learning methods. In this type of scenario, an algorithm receives some [[labeled data]] but most of it remains [[unlabeled]]. The algorithm then utilizes this labeled information to direct its learning process and make predictions about unlabeled data. | ||
===Reinforcement Learning== | ===Reinforcement Learning=== | ||
Reinforcement learning is a type of training in which an algorithm learns from its own actions and the outcomes. In reinforcement learning, an algorithm is given an environment and set of actions it can take within that environment; then it learns which actions produce the most favorable outcomes based on feedback from its environment. | [[Reinforcement learning]] is a type of training in which an algorithm learns from its own actions and the outcomes. In reinforcement learning, an algorithm is given an environment and set of actions it can take within that environment; then it learns which actions produce the most favorable outcomes based on feedback from its environment. | ||
==Role of Training in the Machine Learning Process== | ==Role of Training in the Machine Learning Process== | ||
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==Explain Like I'm 5 (ELI5)== | ==Explain Like I'm 5 (ELI5)== | ||
Picture having a big box of crayons and wanting to teach a robot how to color. Since the robot doesn't know how yet, you must demonstrate for it. | |||
Take out one crayon at a time and show the robot what color it is. Let them try to color with it - it may not match exactly, but that's okay; with | Take out one crayon at a time and show the robot what color it is. Let them try to color with it - it may not match exactly, but that's okay; with your help each time, it becomes better at coloring. | ||
Training a machine learning model is kind of like training a robot. You give it some [[examples]], and it tries to learn from them. The more examples you give it, the better it gets at coloring on its own. | |||
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]] | |||
[[Category:Terms]] [[Category:Machine learning terms]] | |||
Latest revision as of 21:01, 17 March 2023
- See also: Machine learning terms
Introduction
In machine learning, training enables the model to learn from data. The goal of training a model is to determine the optimal parameters (weights and biases) for the model so it can accurately make predictions when presented with new data.
Purpose of Training
Training involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen data. Training allows the model to generalize from what it has seen so that it can accurately make predictions even when presented with unknown variables.
Types of Training
Machine learning employs various types of training, such as supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning.
Supervised Learning
Supervised learning is the most common type of machine learning training. This method involves providing the algorithm with a labeled dataset, in which each data point has an assigned class label. The algorithm then utilizes this labeled data to learn relationships between features in the data and their corresponding labels. Ultimately, supervised learning aims to build a model capable of accurately predicting labels for new, unseen data sets.
Unsupervised Learning
Unsupervised learning is a type of machine-learning training in which an algorithm is given data that has not yet been labeled. The objective is for this algorithm to discover patterns and relationships without the assistance of labeled information. Unsupervised learning is often employed for clustering and dimensionality reduction tasks, as it allows the computer to recognize structure within data that may otherwise go undetected.
Semi-Supervised Learning
Semi-supervised learning is a type of training that incorporates elements from both supervised and unsupervised learning methods. In this type of scenario, an algorithm receives some labeled data but most of it remains unlabeled. The algorithm then utilizes this labeled information to direct its learning process and make predictions about unlabeled data.
Reinforcement Learning
Reinforcement learning is a type of training in which an algorithm learns from its own actions and the outcomes. In reinforcement learning, an algorithm is given an environment and set of actions it can take within that environment; then it learns which actions produce the most favorable outcomes based on feedback from its environment.
Role of Training in the Machine Learning Process
Training is an integral step in machine learning, as it enables the algorithm to learn from data and make accurate predictions. After training is complete, the algorithm can be deployed and used for making new predictions on new data sets. However, training does not happen once; it may need retrained over time as new data becomes available or underlying patterns change within the data.
Explain Like I'm 5 (ELI5)
Training a robot to complete an assignment requires giving it examples and teaching it from those examples. Once trained, the robot can use what it's learned to complete the task on its own even if it has never seen the task before. The more examples provided to your robotic assistant, the better equipped it will become at performing that particular job.
Explain Like I'm 5 (ELI5)
Picture having a big box of crayons and wanting to teach a robot how to color. Since the robot doesn't know how yet, you must demonstrate for it.
Take out one crayon at a time and show the robot what color it is. Let them try to color with it - it may not match exactly, but that's okay; with your help each time, it becomes better at coloring.
Training a machine learning model is kind of like training a robot. You give it some examples, and it tries to learn from them. The more examples you give it, the better it gets at coloring on its own.