The prime raw material for any knowledge representation model is structured data and information. This structured data and information must be effectively fed into machines to enable vigorous training. A machine trained with the help of smaller data sets is susceptible to both under fittings and overfitting. This suggests that the need for large datasets is a must while conceiving intelligent models and systems.
In this article, we look at various methods used for training an AI-powered model. Although any level of training in data science considers the art of conceiving AI models, this article explains such concepts with the help of adequate analogies.
Training AI models
Undoubtedly, data is one of the most critical components of the lifecycle of models that are trained via large data sets. AI models can be trained with the help of three prime techniques or methods. The first one is called supervised learning. The primary aim of supervised learning is to train the system by feeding in labeled inputs. This is similar to the art of giving instructions to the machine to perform in a way that we desire.
Another important way of training AI models is to use the technique of unsupervised learning. Unsupervised learning technique is usually used for training models that largely function in independent working environments. One of the main goals of this technique is to allow the machine to identify patterns in large data sets on its own. In this technique, the machine usually operates after being fed through unlabeled data sets. In addition to this, we may also rely on a different kind of technique called semi-supervised learning. In the semi-supervised learning technique, the machine is usually allowed to train on both supervised and unsupervised learning techniques.
Finally, another unique technique used to train an AI model is the reinforcement learning technique. This technique can be understood with the help of a simple analogy. Consider a person left on an island and is allowed to adapt to this environment without any external input. In the reinforcement learning technique, a machine is allowed to interpret the environmental input on its own, and external input is not given. This is advantageous as it allows the machine to learn independently and even rectify its previous errors based on new inputs and data sets. This optimizes the performance of a machine in the long run and paves the way for intelligent self-improvement.
The road ahead
The various types of techniques described above provide a brief highlight for the training of AI models. These techniques are also a precursor to developing advanced systems with intelligent elements. Such systems are designed to execute an end task with greater efficiency and productivity.