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Table Of Contents
  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning
  • Reinforcement Learning
  • Artificial Neural Network (ANN)

Supervised Learning

Supervised learning is a method in machine learning that is used to create artificial intelligence. In this approach, the code is provided with labeled input data and the output results that are expected. The AI system is trained and told what it should specifically look for. The training continues until the model can identify underlying patterns and associations. Doing this will ensure that the machine produces excellent results when it is presented with new data. Supervised learning strives to understand data towards distinct measurements.

Unsupervised Learning

Supervised learning works in contrast to supervised learning. It involves the use of AI algorithms to detect relationships in datasets. These datasets are neither categorized nor labeled. The AI system is not guided when it is performing any task. Instead, it is expected to classify or group data points within the data on its own. Unsupervised learning algorithms are often used to test AI. These algorithms can perform more intricate tasks than supervised learning algorithms. Unsupervised learning models can be unpredictable. For example, if we used an unsupervised algorithm to sort sheep from goats, it may add a category that is not needed to cover strange breeds. This often creates clutter rather than order.

Deep Learning

Deep learning is a concept used in artificial intelligence and machine learning. It is used to emulate how humans acquire knowledge. Deep learning expedites and makes easy the process of analyzing and interpreting large data. It is extensively applied in predictive modeling and statistics. To understand how deep learning works, imagine a baby whose first word is “cup.” The young one will learn what a cup is and what is not by pointing to items and saying cup. The mom will say “yes, that’s a cup” or no that is not a cup.” As the baby points at a variety of objects, he/she continues to know the features of all cups.

Reinforcement Learning

Reinforcement learning is a topic in machine learning that is used to locate the best action that should be taken for a particular situation. Unlike in supervised learning where the AI model is trained with the correct action, reinforcement learning requires the agent to determine the course of action to take for the given task. The reinforcement agent is expected to learn from its experience. In reinforcement learning, making the right decision maximizes the chance of earning a reward.

Artificial Neural Network (ANN)

Artificial Neural network is a concept that mimics how the neural network of a man’s nervous system works. Dr. Robert Hecht-Nielsen says a neural network is a computer structure that is made up of processing components that are highly-connected yet simple. These elements process data using strong state response inputs from the outside. ANN mimics how the human brain works by connecting wires and silicon. These connections are used to represent dendrites and living neurons. ANN is the building block of artificial intelligence. It aims to resolve problems that are challenging to statistical techniques and humans.