YUFENG GUO: From detecting skin cancer

to sorting cucumbers to detecting escalators

in need of repair, machine learning

has granted computer systems entirely new abilities.

But how does it really work under the hood?

Let's walk through a basic example

and use it as an excuse to talk about the process of getting

answers from your data using machine learning.

Welcome to Cloud AI Adventures.

My name is Yufeng Guo.

On this show, we'll explore the art, science,

and tools of machine learning.

Let's pretend that we've been asked

to create a system that answers the question of whether a drink

is wine or beer.

This question answering system that we build

is called a model, and this model

is created via a process called training.

In machine learning, the goal of training

is to create an accurate model that answers our questions

correctly most of the time.

But in order to train the model, we

need to collect data to train on.

This is where we will begin.

Our data will be collected from glasses of wine and beer.

There are many aspects of drinks that we could collect data on--

everything from the amount of foam to the shape of the glass.

But for our purposes, we'll just pick two simple ones--

the color as a wavelength of light and the alcohol content

as a percentage.

The hope is that we can split our two types of drinks

along these two factors alone.

We'll call these our features from now on--

color and alcohol.

The first step to our process will

be to run out to the local grocery store,

buy up a bunch of different drinks,

and get some equipment to do our measurements-- a spectrometer

for measuring the color and a hydrometer

to measure the alcohol content.

It appears that our grocery store has an electronics

hardware section as well.

Once our equipment and then booze-- we got it all set up--

it's time for our first real step of machine

learning-- gathering that data.

This step is very important because the quality

and quantity of data that you gather

will directly determine how good your predictive model can be.

In this case, the data we collect

will be the color and alcohol content of each drink.

This will yield us a table of color, alcohol content,

and whether it's beer or wine.

This will be our training data.

So a few hours of measurements later, we've

gathered our training data and had a few drinks, perhaps.

And now it's time for our next step of machine learning--

data preparation--

where we load our data into a suitable place

and prepare it for use in our machine learning training.

We'll first put all our data together then randomize

the ordering.

We wouldn't want the order of our data

to affect how we learn since that's not

part of determining whether a drink is beer or wine.

In other words, we want to make a determination of what

a drink is independent of what drink came before or after it

in the sequence.

This is also a good time to do any pertinent visualizations

of your data, helping you see if there

is any relevant relationships between different variables

as well as show you if there are any data imbalances.

For instance, if we collected way more data points about beer

than wine, the model we train will be heavily biased

toward guessing that virtually everything that it sees

is beer since it would be right most of the time.

However, in the real world, the model

may see beer and wine in equal amount, which

would mean that it would be guessing beer wrong half

the time.

We also need to split the data into two parts.

The first part used in training our model

will be the majority of our dataset.

The second part will be used for evaluating our train model's

performance.

We don't want to use the same data that the model was trained

on for evaluation since then it would just

be able to memorize the questions,

just as you wouldn't want to use the questions from your math

homework on the math exam.

Sometimes the data we collected needs other forms

of adjusting and manipulation-- things

like duplication, normalization, error correction, and others.

These would all happen at the data preparation step.

In our case, we don't have any further data preparation needs,

so let's move on forward.

The next step in our workflow is choosing a model.

There are many models that researchers and data scientists

have created over the years.

Some are very well suited for image data, others

for sequences, such as text or music, some for numerical data,

and others for text-based data.

In our case, we have just two features-- color and alcohol

percentage.

We can use a small linear model, which

is a fairly simple one that will get the job done.

Now we move on to what is often considered

the bulk of machine learning--

the training.

In this step, we'll use our data to incrementally improve

our model's ability to predict whether a given

drink is wine or beer.

In some ways, this is similar to someone

first learning to drive.

At first, they don't know how any of the pedals, knobs,

and switches work or when they should be pressed or used.

However, after lots of practice and correcting

for their mistakes, a licensed driver emerges.

Moreover, after a year of driving,

they've become quite adept at driving.

The act of driving and reacting to real-world data

has adapted their driving abilities, honing their skills.

We will do this on a much smaller scale with our drinks.

In particular, the formula for a straight line

is y equals mx plus b, where x is the input,

m is the slope of the line, b is the y-intercept,

and y is the value of the line at that position x.

The values we have available to us to adjust or train

are just m and b, where the m is that slope and b is

the y-intercept.

There is no other way to affect the position of the line

since the only other variables are x, our input, and y,

our output.

In machine learning, there are many m's

since there may be many features.

The collection of these values is usually

formed into a matrix that is denoted

w for the weights matrix.

Similarly, for b, we arranged them together,

and that's called the biases.

The training process involves initializing some random values

for w and b and attempting to predict

the outputs with those values.

As you might imagine, it does pretty poorly at first,

but we can compare our model's predictions with the output

that it should have produced and adjust the values in w

and b such that we will have more accurate predictions

on the next time around.

So this process then repeats.

Each iteration or cycle of updating the weights and biases

is called one training step.

So let's look at what that means more concretely

for our dataset.

When we first start the training,

it's like we drew a random line through the data.

Then as each step of the training progresses,

the line moves step by step closer

to the ideal separation of the wine and beer.

Once training is complete, it's time

to see if the model is any good.

Using evaluation, this is where that dataset that we set

aside earlier comes into play.

Evaluation allows us to test our model

against data that has never been used for training.

This metric allows us to see how the model might

perform against data that it has not yet seen.

This is meant to be representative of how

the model might perform in the real world.

A good rule of thumb I use for a training-evaluation split is

somewhere on the order of 80%-20% or 70%-30%.

Much of this depends on the size of the original source dataset.

If you have a lot of data, perhaps you

don't need as big of a fraction for the evaluation dataset.

Once you've done evaluation, it's

possible that you want to see if you can further improve

your training in any way.

We can do this by tuning some of our parameters.

There were a few that we implicitly

assumed when we did our training,

and now is a good time to go back and test

those assumptions, try other values.

One example of a parameter we can tune

is how many times we run through the training set

during training.

We can actually show the data multiple times.

So by doing that, we will potentially

lead to higher accuracies.

Another parameter is learning rate.

This defines how far we shift the line

during each step based on the information

from the previous training step.

These values all play a role in how accurate our model can

become and how long the training takes.

For more complex models, initial conditions

can play a significant role as well in determining

the outcome of training.

Differences can be seen depending

on whether a model starts off training

with values initialized at zeros versus some distribution

of the values and what that distribution is.

As you can see, there are many considerations

at this phase of training, and it's important

that you define what makes a model good enough for you.

Otherwise, we might find ourselves tweaking parameters

for a very long time.

Now, these parameters are typically

referred to as hyperparameters.

The adjustment or tuning of these hyperparameters

still remains a bit more of an art than a science,

and it's an experimental process that

heavily depends on the specifics of your dataset, model,

and training process.

Once you're happy with your training and hyperparameters,

guided by the evaluation step, it's

finally time to use your model to do something useful.

Machine learning is using data to answer questions,

so prediction or inference is that step where we finally

get to answer some questions.

This is the point of all of this work where the value of machine

learning is realized.

We can finally use our model to predict whether a given

drink is wine or beer, given its color and alcohol percentage.

The power of machine learning is that we

were able to determine how to differentiate between wine

and beer using our model rather than using human judgment

and manual rules.

You can extrapolate the ideas presented today

to other problem domains as well, where

the same principles apply--

gathering data, preparing that data, choosing a model,

training it and evaluating it, doing your hyperparameter

training, and finally, prediction.

If you're looking for more ways to play

with training and parameters, check out

the TensorFlow Playground.

It's a completely browser-based machine learning sandbox,

where you can try different parameters

and run training against mock datasets.

And don't worry, you can't break the site.

Of course, we will encounter more steps and nuances

in future episodes, but this serves

as a good foundational framework to help

us think through the problem, giving us a common language

to think about each step and go deeper in the future.