In basic terms, the goal of using AI is to make computers think as humans do. This may seem like something new, but the field was born in the 1950s.
A common machine learning task is supervised learning , in which you have a dataset with inputs and known outputs. The task is to use this dataset to train a model that predicts the correct outputs based on the inputs. The image below presents the workflow to train a model using supervised learning:
The goal of supervised learning tasks is to make predictions for new, unseen data. To do that, you assume that this unseen data follows a probability distribution similar to the distribution of the training dataset. If in the future this distribution changes, then you need to train your model again using the new training dataset.
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Python AI: How to Build a Neural Network & Make Predictions – Real Python
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Keep reading, keep studying, the more you learn the more you change. If you are doing the Python lessons please join this discord channel https://discord.gg/kugXx9KY but please follow the rules
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Learn more about artificialintelligence with this collection
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Transfer learning and fine tuning
Transfer learning consists of taking features learned on one problem, and leveraging them on a new, similar problem. For instance, features from a model that has learned to identify racoons may be useful to kick-start a model meant to identify tanukis.
- Take layers from...
Common Probability Distributions
The common probability distributions are:
- Uniform Distribution: It is a simple off or on distribution, where anything outside the given range is 0.
- Normal (or Gaussian) Distribution: This distribution has the same standard deviation in all d...
3 Categories of Machine Learning
- Supervised learning: It has a set of labelled data to train the model on. In a way, it means supervising a machine by providing a ton of information about a particular case and giving it the case outcome.
- Unsupervised learning:
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