All language subtitles for 2. Required Background, software, and hardware

This course will be taught entirely in
Python 3 .6.

I'm not being oddly specific here for
its own sake.

There are some intricacies in some of
the packages that require this

version, and Python 3 .7 or later won't
actually work.

So if you're running an earlier or later
version, it's best to create a separate

environment with 3 .6 or just downgrade
or upgrade your global install.

As far as what features of the language
you need to be comfortable with, you

should be proficient with the core
features.

You don't need to know every nuance, but
you should be able to use list

comprehensions, looping, conditionals,
and know a little bit about the basics

inheritance and object -oriented
programming.

With respect to the mathematics, the
more exposure you've had, the better.

While I will put a heavy emphasis on
math in this course, I'll explain

everything you need to know as we go
along.

If you've studied linear algebra before,
then you'll be well equipped to

understand the mechanics of neural
networks.

If not, it's alright.

We'll cover what we need, when we need
it.

A healthy dose of calculus would also be
helpful, as it makes the concept of

gradient descent rather intuitive.

Again, don't worry, since we'll cover
all of that when we need it.

As far as packages are concerned, they
are relatively light.

You'll need PyTorch, TorchVision,

NumPy, Matplotlib, the OpenAI Gym,
Box2Dpy, and AtariPy.

You can set all of this up in a virtual
environment if you like, or just install

all of it to your default Python
environment.

You may notice that I didn't mention
Jupyter as a requirement.

To be honest, I'm not a big fan of
Jupyter notebooks, and we won't be using

for this course.

They are pretty good for doing
demonstrations and simple lessons.

but they have a number of shortcomings
for serious programming.

If you want to put all of your code and
some video into a Jupyter notebook to

show off on your GitHub, that's a great
idea, but my solutions will all be in

Python files.

You can use the editor of your choice.

I'll be using Atom as its relatively
lightweight and open source software.

VS Code is another great option, and
it's available for all the major

systems.

Sublime is a great choice, though it is
Nagware.

Bonus points if you use Vim.

Let's address the elephant in the room,
the hardware.

Make no mistake, DeepQ learning is
resource intensive.

For best results, you'll need an NVIDIA
GPU of at least the Kepler generation or

better, which is the GTX 700 series and
above.

I know that many of you won't meet this
requirement, and that's fine, you can

still complete the course.

For those that don't have a heavy
hitting GPU, I recommend sticking to the

environment.

It's simple enough that a modern CPU can
learn to achieve world -class

performance in a reasonable amount of
time.

If you have an adequate GPU, please feel
free to apply these algorithms to as

many environments as you want.

The choice of environment doesn't affect
the underlying code, so you'll learn

just as much by having an agent beat
Pong as you will Breakout or any of the

other games in the Atari library.

For several environments, I'll provide
pre -trained models on my GitHub. You

download these and play with them as you
see fit.

Okay, that's all for now.

While you don't need to be a master of
Python to succeed in this course,

familiarity with its core functionality
is required.

A strong mathematical background is
certainly helpful, but it is by no means

required.

The list of required packages is
relatively light, just make sure you're

Python 3 .6 and a reliable code editor.

In the next video, we're going to talk
about how to succeed in this course.