Hi this is Jeff Heaton. Welcome to my course, Applications
of Deep Neural Networks.  This course is
taught at Washington University in a hybrid format.
Even though this class is designed primarily
for students of Washington University, you can also get quite a bit
from this course simply by watching the videos
and following along as an internet user.
This is the course website, it links right to
a GitHub repository that contains
all of the course information. Everything in this class
is a Jupyter notebook, it is taught in Python
using Keras for deep learning.
For example, if you look at the very first class session
you will see all of the information that is covered.
all of the code is right here in Jupyter notebooks.
and you are shown how to actually execute and work with it
throughout this course.
Everything that you need for this course is contained on
these class notes that I have in Jupyter notebooks.
Here you can see many of the topics that are covered in them.
We provide an introduction into Python, it helps
if you've taken another programming language prior to
this course, but I do show you just enough Python
to get you started in the area of deep learning.
if you do not know much about Python, or
in particular computer programming, you will defiantly
find this course somewhat challenging.
as you go through the first classes.
We learn about training a neural network, classification and regression
backpropagation, convolutional neural networks,
used for computer vision.
There is also a Kaggle competition,
we learn about regularization, long short term memory (LSTM),
LSTM for natural language processing (NLP)
and time series.
Specifically we look at natural language processing (NLP)
Take a look at security, and some advanced
techniques that I change each semester,
depending on what recent research is going on.
and we look at how you can run very high-scale
 
neural networks using AWS, Google cloud, that sort of thing.
One of the more interesting and exciting
aspects of this class is that we actually do a Kaggle competition.
This is an in-class Kaggle competition
so it is a data set that I created for the students to compete
against.  Here you see the Kaggle
competition for the fall 2018
course that is currently underway
there are currently ten teams competing
made up of both my students, and sometimes
other people from the Internet, and just two days to go.
If you look at the leaderboard, you can see how these students
are... are ranked.
this is an RMSE score, lower is better.
Some students have done more
than others
There are also 10 programming assignments that occur throughout this
semester. Each of them can be automatically
assessed by a program
that allows you to submit your assignment and
have it checked to give you an idea
of what grade you would get before it... this helps you
to learn, and eliminate simple mistakes
that would cost you points.
Well, this is the deep learning course, whether
you are a traditional student of Washington University or somebody
following along on the Internet, Ihope you
Find this material very useful, thank you!
And if you would like to see additional material, that I put out
please subscribe to my YouTube channel.
