machine learning

Artificial intelligence is the future. Artificial intelligence is science fiction. Artificial intelligence is already part of our everyday lives.

Artificial Intelligence is the general category, common between deep learning and machine learning. In a diagram, Artificial Intelligence would be the bigger, encapsulating circle that contains Machine and Deep Learning. AI is basically any intelligence demonstrated by a machine that leads it to an optimal or suboptimal solution given a problem.

What is Machine Learning?

Tom Mitchell: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E ”.

It’s not that complicated. Machine Learning, at its core, is really just making a line of best fit, except in many dimensions. A house price prediction model looks at a ton of data, with each data point having several dimensions like size, bedroom count, bathroom count, yard space, etc. It creates a function out of these input parameters, and then just shifts the coefficients to each of these parameters as it looks at more and more data.

This method of Machine Learning is called “Supervised Learning,” where the data given to the model includes the answer to the problem for each input set. It’s basically giving the input parameters, called features, and the outputs for each set of features, from which the model adjusts its function to match data. Then, when given any other input data, the model can execute the same function and come up with an accurate output.

Other factions of Machine Learning are Unsupervised Learning and Reinforcement Learning. Concisely, Unsupervised Learning just finds similarities in data. In our house example, the data wouldn’t include house prices the data would only be input, it would have no output. The model would be able to say “well based on these parameters, House 1 is most similar to House 3” or something of the sort, but wouldn’t be able to predict the price of a given house.

What is Deep Learning?

The concept of deep learning is not new. It has been around for a couple of years now. But nowadays with all the hype, deep learning is getting more attention. “Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts, with each concept defined in relation to simpler concepts, and more abstract representations computed in terms of less abstract ones.”

Deep learning was inspired by the structure and function of the brain, namely the interconnecting of many neurons. Neural Networks are algorithms that mimic the biological structure of the brain.

Deep Learning is basically Machine Learning on steroids. There are multiple layers to process features, and generally, each layer extracts some piece of valuable information. For example, one neural net could process images for steering a self-driving car. Each layer would process something different, like, for example, the first could be detecting edges for the sides of the road. Another layer could be detecting the lane lines in the image, and another possibly other cars.

The Difference Between Machine Learning and Deep Learning

You May Also Like

Leave a Reply

Your email address will not be published. Required fields are marked *