October 23, 2019 69

How to Make a Prediction – Intro to Deep Learning #1

How to Make a Prediction – Intro to Deep Learning #1


Hello world, it’s Seraj and
welcome to Intro to Deep Learning. In this first episode, we’ll predict an animal’s body weight
given only the weight of its brain. This course will be four months long and
it’ll all be released on my channel. I have a live session every
Wednesday at 10 am PST that explains every week’s topic in depth. And I’m collaborating with Udacity
to offer a Nanodegree to those that successfully complete this course. This course is for anyone who wants to
learn how to harness the incredible power of neural networks and
become a deep learning engineer. You don’t have to be an experienced
developer or mathematician. The only prerequisite for this
course is knowing quantum mechanics. Just kidding, only basic Python syntax. We’ll learn what we need to along
the way by building an AI that can do everything from predicting
the price of Tesla stock to painting surrealist masterpieces. Traditionally, programming has been
about defining every single step for a program to reach an outcome. The machine only flips that approach. With machine learning,
we define the outcome and the program learns
the steps to get there. So, if I wanted to build an app that can
recognize California license plates. And started writing out code to
recognize the hundreds of different features of a license plate like the
shape of certain letters and the colors. We just say, here are some examples
of a California license plate, learn the steps you need so
that you can recognize it. Or if I wanted to make a bot
that could beat Super Mario, instead of writing code for
every possible scenario like jumping and it’s running towards you, we’d say the
goal is to get to the endpoint without dying, learn the steps to get there. And sometimes we don’t even have
an idea what steps could possibly be. Like if we’re a bank and we suspect
there’s some kind of fraudulent activity happening but we’re not
sure exactly how to detect that or even know what to look for, we can say,
here’s a log of all user activity. Find the users that are unlike the rest,
and it will learn the steps to
detect the anomalies by itself. Machine learning is already
everywhere on the Internet. Every major service uses it in some way. In fact, you could be using
it right now to decide which other videos you might
like as you watch this. And its uses will only grow over time. It will be embedded in all of
our Internet connected devices. Everything from fridges,
to cars, to personal assistance. Re-learning and adapting to our needs. And you know that rule that says you
need 10,000 hours to master any skill or we’ll just be able to off load that
training time to our machines and it’ll give us super powers instantly. Anyone will be able to
compose a symphony. Alexa I feel melancholic. Make me a piano piece for this. [MUSIC] And have a beat to it. Yo. Anyone will be able to direct a movie. Okay, Hugo recreate Star Wars but
put me in it. I doctorize most of my code now.>>You’re breaking my heart.>>Damn right. With machine learning,
if you can trim it, it can exist. And the field is currently
advancing very fast as researchers build on each other’s work. My neuronet sucks. Go deeper. Wow, I just achieved state of the art. There are a lot of machine
running models out there and one of them is called a Neural Network. When we use a neural network
that’s not just one or two, but many layers deep to make a
prediction, we call that deep learning. It’s a subset of machine
learning that has outperformed almost every other type of model almost
every time on a huge range of tasks. We’ll dive into deep learning
more in the next episode but this video will just focus on
machine learning in general. We usually class learning
into three different styles. The first style is called
supervised learning. It’s where we give a model a labeled
data set like car pictures so it gets feedback on what’s correct and
what’s not. It just have to learn the mapping
between the labels and the data. And then they can solve some given
task like classifying the type of car in an image. It’s all relatively straight forward and we’ve gotten the incredible
results from it. The second learning style is
called unsupervised learning. This is when we give a model
a data set without labels, it gets no feedback on what’s correct or
not. It has to learn by itself, what the structure of the data
is to solve some given task. This is harder to do but
more convenient, since not everyone has a perfectly
labeled data set sitting around. Most data is unlabeled. It’s messy and complex. And the third type is
reinforcement learning. This is where a model isn’t given
feedback right off the bat, it only gets it if it achieves its goal. So if we’re trying to create
a reinforcement learning bot that can learn to beat humans at chess,
it would only receive feedback if it won the game whereas in the supervised
approach we get feedback every move and in the unsupervised approach we’d
never get feedback, even if it won. Unlike the other two learning styles,
reinforcement learning is linked to the idea of interacting with
an environment through trial and error. So I’ve got a data set of
measurements of different animals and we want to predict an animals body
weight given its brain weight. Since our data is labeled,
this will be a supervisor approach and the type of machine learning task
will perform is called regression. We’ll write out a 10 line
Python script to do this and I’ll explain things as we go. We’ll start off by importing
our three dependencies. The first one is pandas which
will let us read our data set. The second one is scikit learn which
is the machine learning library we’re using for this example. And a third is matplotlib which will
let us visualize our model and data. Now that we’ve imported
our dependencies, we can read our data set using pandas. We’ll use the read fwf function
to read our animal data set, a table of fixed width formatted
lines into a pandas data frame object which is a 2D data structure of rows and
columns. Our data set contains the average
brain and body weight for a number of animal species. Once our data is in our dataframe
variables, we can easily parse and read both measurements into
two separate variables. We’ll store our brain measurements
in the x_values variable and the body measurements in
the y_values variable. So, if we were to plot this data
right now on a standard 2D graph, it would look like this. And our goal is that given a new
animal’s body weight will be able to predict what its brain size is. So how are we going to do that? Yeah, uh-huh. You know what it is. Independent and dependent,
independent and dependent. My data’s got the values for
the brain and the body weight. And i’m wondering what to
use to find if they relate. Linear Regression. Helps find the relationship. We’re going to measure it and
find the only line of best fit. The equation y=mx +b is all we need. B is the y intercept and
m measures how steep. Plied it on the graph,
let us predict the body with the brain. Low hair, pop champagne.>>When you set to learn linear
model object to initialize our linear regression and store it
in the body regression variable. Then we can fit our model
on our x y value pairs. Now that we have the line that
best fit we can plot our x, y value pairs on a scatter plot. Then plot our regression line by
saying for every x value we have predict the associated y value and draw
a line that intersects all those points. We can then display it
using the show function. Let’s go ahead compile
this code in terminal. Our scatter plot will appear with
all our data points mapped out. The x axis represents brain weights and
the y axis represents body weights. Our regression lines seems to fit most
of the data pretty well and there seems to be a very strong correlation here
between brain weight and body weight. And as we move along the line, given any brain weight we can also
predict the associated body weight. So to break it down while traditional
programming is about defining the steps to reach an outcome, machine learning
is about defining the outcome. And our program will learn
the steps to get there. There are three different learning
styles, supervised, unsupervised and reinforcement learning. And linear regression models, the relationship between
an independent and dependent variable to create the line of best fit which
we can then use to make predictions. The winner of last week’s
Coding Challenge is Mick Van Hulst. He modified the game
world to be more complex. And his queue learning bot was much more
efficient at reaching the goal than my demo bot. Wizard of the week. And the runner up is Vishal Batchu. He generated maps using
cellular automata. The challenge for this video is to use
scikit-learn to create a regression line for a different data set
that I’ll provide, and print out the error between your
prediction and the actual value. Details are in the Readme, post your
GitHub link in the comments and I’ll announce the winner in one week. Please hit that Subscribe button and for now, I’ve gotta feel to learn,
so thanks for watching.

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