Introduction to neural networks

It is time to set our goals and introduce the framework we will use creating a machine learning algorithm

ultimately means building a model that outputs correct information.

Given that we’ve provided input data for now think of this model as a black box we feed input and it

delivers an output.

For instance we may want to create a model that predicts the weather tomorrow given meteorological information

for the past few days.

The input will feed to the model could be metrics such as temperature humidity and precipitation.

The output we will obtain would be the weather forecast for tomorrow.

Now before we get comfortable and confident about the models output we must train the model.

Training is a central concept in machine learning as this is the process through which the model learns

how to make sense of the input data.

Once we have trained our model we can simply feed it with data and obtain an output.

All right the basic logic behind training and algorithm involves four ingredients.

Data Model objective function and an optimization algorithm.

Let’s explore each of them.

First we must prepare a certain amount of data to train with.

Usually this is historical data which is readily available.

Second we need a model.

The simplest model we can train is a linear model in the weather forecast example.

That would mean to find some coefficients multiply each variable with them and sum everything to get

the output as we will see later though the linear model is just the tip of the iceberg.

Stepping on the linear model DB machine learning lets us create complicated non-linear models.

They usually fit the data much better than a simple linear relationship.

Awesome.

The third ingredient is the objective function.

So far we took data fit it to the model and obtained an output.

Of course we want this output to be as close to reality as possible.

That’s where the objective function comes in.

It estimates how correct the models outputs are.

On average the entire machine learning framework boils down to optimizing this function.

For example if our function is measuring the prediction error of the model we would want to minimize

this error or in other words minimize the objective function OK.

Our final ingredient is the optimization algorithm.

It consists of the mechanics through which we vary the parameters of the model to optimize the objective

function.

For instance if our weather forecast model is weather tomorrow equals w 1 times temperature plus W 2

times humidity the optimization algorithm may go through values like 1 point 0 5 times temperature plus

1.2 times humidity or 1 point zero five times temperature minus 1.2 times humidity or 1 point zero four

times temperature minus 1.1 nung times humidity and so on.

W 1 and w 2 are the parameters that will change for each set of parameters.

We would calculate the objective function then we would choose the model with the highest predictive

power.

How do we know which one is the best.

Well it would be the one with an optimal objective function wouldn’t it.

All right.

Great.

Later we’ll reiterate what we’ve said here because there will be separate lessons for the ingredients

of an algorithm.

Did you notice we said four ingredients instead of saying four steps.

This is intentional as the machine learning process is iterative.

We feed data into the model and compare the accuracy through the objective function.

Then we vary the model parameters and repeat the operation.

When we reach a point after which we can no longer optimize or we don’t need to we would stop since

we would have found a good enough solution to our problem in this section.

We look at each of the basic building blocks in more detail at the end.

We will conclude by creating our first machine learning algorithm.

Sounds exciting.

Well it definitely is.

Thanks for watching.