Machine learning is one term that has created an immense amount of buzz in the technology industry. With its enormous potential in healthcare, medical diagnosis as well as solving complex business problems, machine learning has revolutionised many aspects of human lives.
However, as the technology is evolving and becoming more complex, it comes up with exciting subfields and terminologies associated with it. In this article, we are going to decode some of the most used jargons in machine learning.
Also Read: 25 AI Terminologies & Jargons You Must Assimilate To Sound Like A Pro
Autoregression
Autoregression is a phenomenon in machine learning, where an autoregressive model learns from a series of times steps, aka a time series model that uses information from previous timed steps as input to a regression equation in order to predict the value. With autoregression, one can predict accurate forecasts on a range of time series problems. It works based on determining the correlation of previous time steps, also known as the variables, among each other, which in turn helps in predicting the output. If both the variables changed in the same direction, there could be a positive correlation; however, if both turned to a different direction, then it will be termed as negative — either of the ways the relationship between the result and the input can be easily determined. With a higher correlation, the more chances of predicting the outcome from the past information. To understand better read: Python Library For Time Series Analysis And Prediction.
Backpropagation
In machine learning, backpropagation is also known as “backward propagation of errors,” and is an algorithm used for training artificial neural networks for supervised learning. It works by determining the minimum value of the error at the output and then propagating it back into the neural network. Backpropagation is a critical process of neural net training, where it is leveraged for fine-tuning the weights based on the error rates. Proper modification of weights will help in minimising the errors, making the model reliable.
Not only this method is fast and easy to program, but also needs no parameters to tune and no prior knowledge about the network. Static backpropagation and recurrent backpropagation are the two types of backpropagation networks. Although it comes with many benefits, the only drawback is the sensitivity of the method for noisy data.
You can also read what Geoff Hinton thinks of Backpropagation.
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