Training
- 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 types of training, and its role within it.
Purpose of Training
Training in machine learning is the primary goal of this process, which allows the algorithm to learn from data provided. This involves discovering patterns and relationships amongst the data which can be used for making predictions about unseen information. Training allows the algorithm 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 type of 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)
Sure! 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 some help from you, each time becomes better at coloring.
Similar to teaching a robot to draw, providing examples and encouraging it to learn from them is similar. The more examples you give it, the better at coloring on its own.
Just like with coloring, robots have many skills they can master. Some robots learn to recognize objects in pictures, some play games and some even write stories like me!