Learn more about artificialintelligence with this collection
Understanding machine learning models
Improving data analysis and decision-making
How Google uses logic in machine learning
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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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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