User talk:Admin

From ElixirBlocks
Revision as of 15:11, 4 January 2024 by Admin (talk | contribs)
Jump to: navigation, search

What is the role of a Developer in ML?

With normal code, a human writes the code directly, and a computer reads and inter‐ prets that code, or some derivative of it.

In Machine Learning, a human writes the algorithm’s trainer. Assisted by a frame‐ work, or even from scratch, a human outlines in code the parameters of the problem, the desired structure, and the location of the data to learn from. Now the machine runs this program-training program, which continuously writes an ever-improving algorithm as the solution to that problem. At some point, you stop this program and take the latest algorithm result out and use it.

The algorithm is much smaller than the data that was used to create it.


Types of Machine Learning

Supervised

Most common form of learning. Supervised ML simply means that we have an answer key for every question we’re using to train our machine. That is to say, our data is labeled. So if we’re trying to teach a machine to distinguish if a photo contains a bird, we can immediately grade the AI on whether it was right or wrong. Like a Scantron, we have the answer key. But unlike a Scantron and because it’s probability math, we can also identify how wrong the answer was.

Unsupervised

Unsupervised learning doesn’t require us to have an answer key. We only need ques‐ tions. Unsupervised machine learning would be ideal, as most information in the world does not come with labels. This category of machine learning focuses on what a machine could learn and report from unlabeled data.

Semisupervised

Semi-supervised learning is a broad category of machine learning that uses labeled data to ground predictions, and unlabeled data to learn the shape of the larger data distribution. Practitioners can achieve strong results with fractions of the labeled data, and as a result, can save valuable time and money.

Reinforcement

Reinforcement learning (RL) is a machine learning (ML) technique that trains software to make decisions to achieve the most optimal results. It mimics the trial-and-error learning process that humans use to achieve their goals.