Motivation behind Machine Learning

Sometimes we encounter problems which are really hard to solve by writing a computer program. For example, let's say we wanted to write a computer program to recognize handwritten digits:

Source: MNIST handwritten database

Why should I read this, and what will I learn?

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.

I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.

This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.

By the end of this post, you’ll:

Understand the basic logical framework of ...

Read more…(4369 words)

Category: Deep Learning

4 Comments

comment in this discussion

Noah YonackData Science @ SafeGraph. Harvard ’17. Interested in machine learning and organizational behavior. · 2y

Thank you for the clarification! Super helpful. To further clarify, a simple linear regression model has the form y = b + mX, whereas a multiple linear regression model has the form y = b + (m_1)(X_1) + (m_2)(X_2) + ... + (m_p)(X_p), where m_1, for instance, is the coefficient applied to the first predictor (height, for example), m_2 is the coefficient applied to the second predictor (weight, for example), and so on.*

You can imagine simplifying this long equation by combining all the m's into a vector and all the X's into a vector and then taking the dot product, or you can just think of it as a long equation like the one I wrote out above.

*(As a small side note, these two equations that I wrote out also have another parameter, which is called the error, but I choose to leave that out so as to keep things simple.)

Why should I read this, and what will I learn?

Machine learning is a field that threatens to both augment and undermine exactly what it means to be human, and it’s becoming increasingly important that you—yes, you—actually understand it.

I don’t think you should need to have a technical background to know what machine learning is or how it’s done. Too much of the discussion about this field is either too technical or too uninformed, and, through this blog, I hope to level the playing field.

This is for smart, ambitious people who want to know more about machine learning but who don’t care about the esoteric statistical and computational details underlying the field. You don’t need to know any math, statistics, or computer science to read and understand it.

By the end of this post, you’ll:

Understand the basic logical framework of ...