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Deep Learning A-Z™: Hands-On Artificial Neural Networks

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Description

*** As
seen
on
Kickstarter
***

Artificial
intelligence
is
growing
exponentially.
There
is
no
doubt
about
that.
Self-driving
cars
are
clocking
up
millions
of
miles,
IBM
Watson
is
diagnosing
patients
better
than
armies
of
doctors
and
Google
Deepmind’s
AlphaGo
beat
the
World
champion
at
Go

a
game
where
intuition
plays
a
key
role.

But
the
further
AI
advances,
the
more
complex
become
the
problems
it
needs
to
solve.
And
only
Deep
Learning
can
solve
such
complex
problems
and
that’s
why
it’s
at
the
heart
of
Artificial
intelligence.


Why
Deep Learning
A-Z?

Here
are
five
reasons
we
think
Deep
Learning
A-Z™ really
is
different,
and
stands
out
from
the
crowd
of other
training
programs
out
there:

1.
ROBUST
STRUCTURE 

The
first
and
most
important
thing
we
focused
on
is
giving
the
course
a
robust
structure.
Deep
Learning
is
very
broad
and
complex
and
to
navigate
this
maze
you
need
a
clear
and
global
vision
of
it.

That’s
why
we
grouped
the
tutorials
into
two
volumes,
representing
the
two
fundamental
branches
of
Deep
Learning:
Supervised
Deep
Learning
and
Unsupervised
Deep
Learning. With
each
volume
focusing
on
three
distinct
algorithms,
we
found
that
this
is
the
best
structure
for
mastering
Deep
Learning.

2.
INTUITION
TUTORIALS

So
many
courses
and
books
just
bombard
you
with
the
theory,
and
math,
and
coding…
But
they
forget
to
explain,
perhaps,
the
most
important
part: why
you
are
doing
what
you
are
doing. 
And
that’s
how
this
course
is
so
different.
We
focus
on
developing
an
intuitive *feel* for
the
concepts
behind
Deep
Learning
algorithms.

With
our
intuition
tutorials
you
will
be
confident
that
you
understand
all
the
techniques
on
an
instinctive
level.
And
once
you
proceed
to
the
hands-on
coding
exercises
you
will
see
for
yourself
how
much
more
meaningful
your
experience
will
be. This
is
a
game-changer.

3.
EXCITING
PROJECTS

Are
you
tired
of
courses
based
on
over-used,
outdated
data
sets?

Yes?
Well
then
you’re
in
for
a
treat.

Inside
this
class
we
will
work
on
Real-World
datasets,
to
solve
Real-World
business
problems.
(Definitely
not
the
boring
iris
or
digit
classification
datasets
that
we
see
in
every
course).
In
this
course
we
will
solve
six
real-world
challenges:

  • Artificial
    Neural
    Networks
    to
    solve
    a
    Customer
    Churn
    problem
  • Convolutional
    Neural
    Networks
    for
    Image
    Recognition
  • Recurrent Neural
    Networks
    to
    predict
    Stock
    Prices
  • Self-Organizing
    Maps
    to
    investigate
    Fraud
  • Boltzmann
    Machines
    to
    create
    a
    Recomender
    System
  • Stacked
    Autoencoders*
    to
    take
    on
    the
    challenge
    for
    the Netflix
    $1
    Million
    prize

*Stacked
Autoencoders
is
a
brand
new
technique
in
Deep
Learning
which
didn’t
even
exist
a
couple
of
years
ago.
We
haven’t
seen
this
method
explained
anywhere
else
in
sufficient
depth.

4.
HANDS-ON
CODING 

In Deep
Learning
A-Z™
we
code
together
with
you. Every
practical
tutorial
starts
with
a
blank
page
and
we
write
up
the
code
from
scratch.
This
way
you
can
follow
along
and
understand
exactly
how
the
code
comes
together
and
what
each
line
means.

In
addition,
we
will
purposefully
structure
the
code
in
such
a
way
so
that
you
can
download
it
and
apply
it
in
your
own
projects. Moreover,
we
explain
step-by-step
where
and
how
to
modify
the
code
to
insert
YOUR
dataset,
to
tailor
the
algorithm
to
your
needs,
to
get
the
output
that
you
are
after.

This
is
a
course
which
naturally
extends
into
your
career.

5.
IN-COURSE
SUPPORT

Have
you
ever
taken
a
course
or
read
a
book
where
you
have
questions
but
cannot
reach
the
author?

Well,
this
course
is
different.
We
are
fully
committed
to
making
this
the
most
disruptive
and
powerful
Deep
Learning
course
on
the
planet.
With
that
comes
a
responsibility
to
constantly
be
there
when
you
need
our
help.

In
fact,
since
we
physically
also
need
to
eat
and
sleep
we
have
put
together
a
team
of
professional
Data
Scientists
to
help
us
out.
Whenever
you
ask
a
question
you
will
get
a
response
from
us
within
48
hours
maximum.

No
matter
how
complex
your
query,
we
will
be
there. The
bottom
line
is
we
want
you
to
succeed.

— The
Tools —

Tensorflow
and
Pytorch
are
the
two
most
popular
open-source
libraries
for
Deep
Learning.
In
this
course
you
will
learn
both!

TensorFlow
was
developed
by
Google
and
is
used
in
their
speech
recognition
system,
in
the
new
google
photos
product,
gmail,
google
search
and
much
more.
Companies
using
Tensorflow
include
AirBnb,
Airbus,
Ebay,
Intel,
Uber
and
dozens
more.

PyTorch
is
as
just
as
powerful
and
is
being
developed
by
researchers
at
Nvidia
and
leading
universities:
Stanford,
Oxford,
ParisTech.
Companies
using
PyTorch
include
Twitter,
Saleforce
and
Facebook.

So
which
is
better
and
for
what?

Well,
in
this
course
you
will
have
an
opportunity
to
work
with
both
and
understand
when
Tensorflow
is
better
and
when
PyTorch
is
the
way
to
go.
Throughout
the
tutorials
we
compare
the
two
and
give
you
tips
and
ideas
on
which
could
work
best
in
certain
circumstances.

The
interesting
thing
is
that
both
these
libraries
are
barely
over
1
year
old.
That’s
what
we
mean
when
we
say
that
in
this
course
we
teach
you
the
most
cutting
edge
Deep
Learning
models
and
techniques.


More Tools —

Theano is
another
open
source
deep
learning
library.
It’s
very
similar
to
Tensorflow
in
its
functionality,
but
nevertheless
we
will
still
cover
it.

Keras is
an
incredible
library
to
implement
Deep
Learning
models.
It
acts
as
a
wrapper
for
Theano
and
Tensorflow.
Thanks
to
Keras
we
can
create
powerful
and
complex
Deep
Learning
models
with
only
a
few
lines
of
code.
This
is
what
will
allow
you
to
have
a
global
vision
of
what
you
are
creating.
Everything
you
make
will
look
so
clear
and
structured
thanks
to
this
library,
that
you
will
really
get
the
intuition
and
understanding
of
what
you
are
doing.


Even
More Tools —

Scikit-learn the
most
practical
Machine
Learning
library.
We
will
mainly
use
it:

  • to
    evaluate
    the
    performance
    of
    our
    models
    with
    the
    most
    relevant
    technique,
    k-Fold
    Cross
    Validation
  • to
    improve
    our
    models
    with
    effective
    Parameter
    Tuning
  • to
    preprocess
    our
    data,
    so
    that
    our
    models
    can
    learn
    in
    the
    best
    conditions

And
of
course,
we
have
to
mention
the
usual
suspects.
This
whole
course
is
based
on
Python
and
in
every
single
section
you
will
be
getting
hours
and
hours
of
invaluable
hands-on
practical
coding
experience.

Plus,
throughout
the
course
we
will
be
using Numpy
to
do
high
computations
and
manipulate
high
dimensional
arrays, Matplotlib
to
plot
insightful
charts
and Pandas
to
import
and
manipulate
datasets
the
most
efficiently.


Who
Is
This
Course
For? —

As
you
can
see,
there
are
lots
of
different
tools
in
the
space
of
Deep
Learning
and
in
this
course
we
make
sure
to
show
you
the
most
important
and
most
progressive
ones
so
that
when
you’re
done
with
Deep
Learning
A-Z™
your
skills
are
on
the
cutting
edge
of
today’s
technology.

If
you
are
just
starting
out
into
Deep
Learning, then
you
will
find
this
course
extremely
useful.
Deep
Learning
A-Z™
is
structured
around
special
coding
blueprint
approaches
meaning
that
you
won’t
get
bogged
down
in
unnecessary
programming
or
mathematical
complexities
and
instead
you
will
be
applying
Deep
Learning
techniques
from
very
early
on
in
the
course. You
will
build
your
knowledge
from
the
ground
up
and
you
will
see
how
with
every
tutorial
you
are
getting
more
and
more
confident.

If
you
already
have
experience
with
Deep
Learning, you
will
find
this
course
refreshing,
inspiring
and
very
practical.
Inside
Deep
Learning
A-Z™
you
will
master
some
of
the
most
cutting-edge
Deep
Learning
algorithms
and
techniques
(some
of
which
didn’t
even
exist
a
year
ago)
and
through
this
course
you
will
gain
an
immense
amount
of
valuable
hands-on
experience
with
real-world
business
challenges.
Plus,
inside
you
will
find
inspiration
to
explore
new
Deep
Learning
skills
and
applications.

— Real-World
Case
Studies

Mastering
Deep
Learning
is
not
just
about
knowing
the
intuition
and
tools,
it’s
also
about
being
able
to
apply
these
models
to
real-world
scenarios
and
derive
actual
measurable
results
for
the
business
or
project.
That’s
why
in
this
course
we
are
introducing
six
exciting
challenges:

#1
Churn
Modelling
Problem

In
this
part
you
will
be
solving
a
data
analytics
challenge
for
a
bank.
You
will
be
given
a
dataset
with
a
large
sample
of
the
bank’s
customers.
To
make
this
dataset,
the
bank
gathered
information
such
as
customer
id,
credit
score,
gender,
age,
tenure,
balance,
if
the
customer
is
active,
has
a
credit
card,
etc.
During
a
period
of
6
months,
the
bank
observed
if
these
customers
left
or
stayed
in
the
bank.

Your
goal
is
to
make
an
Artificial
Neural
Network
that
can
predict,
based
on
geo-demographical
and
transactional
information
given
above,
if
any
individual
customer
will
leave
the
bank
or
stay
(customer
churn).
Besides,
you
are
asked
to
rank
all
the
customers
of
the
bank,
based
on
their
probability
of
leaving.
To
do
that,
you
will
need
to
use
the
right
Deep
Learning
model,
one
that
is
based
on
a
probabilistic
approach.

If
you
succeed
in
this
project,
you
will
create
significant
added
value
to
the
bank.
By
applying
your
Deep
Learning
model
the
bank
may
significantly
reduce
customer
churn.

#2
Image
Recognition

In
this
part,
you
will
create
a
Convolutional
Neural
Network
that
is
able
to
detect
various
objects
in
images.
We
will
implement
this
Deep
Learning
model
to
recognize
a
cat
or
a
dog
in
a
set
of
pictures.
However,
this
model
can
be
reused
to
detect
anything
else
and
we
will
show
you
how
to
do
it

by
simply
changing
the
pictures
in
the
input
folder.

For
example,
you
will
be
able
to
train
the
same
model
on
a
set
of
brain
images,
to
detect
if
they
contain
a
tumor
or
not.
But
if
you
want
to
keep
it
fitted
to
cats
and
dogs,
then
you
will
literally
be
able
to
a
take
a
picture
of
your
cat
or
your
dog,
and
your
model
will
predict
which
pet
you
have.
We
even
tested
it
out
on
Hadelin’s
dog!

#3 Stock
Price
Prediction

In
this
part,
you
will
create
one
of
the
most
powerful
Deep
Learning
models.
We
will
even
go
as
far
as
saying
that
you
will
create
the
Deep
Learning
model
closest
to “Artificial
Intelligence”
.
Why
is
that?
Because
this
model
will
have
long-term
memory,
just
like
us,
humans.

The
branch
of
Deep
Learning
which
facilitates
this
is
Recurrent
Neural
Networks.
Classic
RNNs
have
short
memory,
and
were
neither
popular
nor
powerful
for
this
exact
reason.
But
a
recent
major
improvement
in
Recurrent
Neural
Networks
gave
rise
to
the
popularity
of
LSTMs
(Long
Short
Term
Memory
RNNs)
which
has
completely
changed
the
playing
field. We
are
extremely
excited
to
include
these
cutting-edge
deep
learning
methods
in
our
course!

In
this
part
you
will
learn
how
to
implement
this
ultra-powerful
model,
and
we
will
take
the
challenge
to
use
it
to
predict
the
real
Google
stock
price.
A
similar
challenge
has
already
been
faced
by
researchers
at
Stanford
University
and
we
will
aim
to
do
at
least
as
good
as
them.

 #4 Fraud
Detection

According
to
a
recent
report
published
by
Markets
&
Markets
the
Fraud
Detection
and
Prevention
Market
is
going
to
be
worth
$33.19
Billion
USD
by
2021.
This
is
a
huge
industry
and
the
demand
for
advanced
Deep
Learning
skills
is
only
going
to
grow.
That’s
why
we
have
included
this
case
study
in
the
course.

This
is
the
first
part
of
Volume
2

Unsupervised
Deep
Learning
Models.
The
business
challenge
here
is
about
detecting
fraud
in
credit
card
applications.
You
will
be
creating
a
Deep
Learning
model
for
a
bank
and
you
are
given
a
dataset
that
contains
information
on
customers
applying
for
an
advanced
credit
card.

This
is
the
data
that
customers
provided
when
filling
the
application
form.
Your
task
is
to
detect
potential
fraud
within
these
applications.
That
means
that
by
the
end
of
the
challenge,
you
will
literally
come
up
with
an
explicit
list
of
customers
who
potentially
cheated
on
their
applications.

#5
&
6
Recommender
Systems

From
Amazon
product
suggestions
to
Netflix
movie
recommendations

good
recommender
systems
are
very
valuable
in
today’s
World.
And
specialists
who
can
create
them
are
some
of
the
top-paid
Data
Scientists
on
the
planet.

We
will
work
on
a
dataset
that
has
exactly
the
same
features
as
the
Netflix
dataset:
plenty
of
movies,
thousands
of
users,
who
have
rated
the
movies
they
watched.
The
ratings
go
from
1
to
5,
exactly
like
in
the
Netflix
dataset,
which
makes
the
Recommender
System
more
complex
to
build
than
if
the
ratings
were
simply
“Liked”
or
“Not
Liked”.

Your
final
Recommender
System
will
be
able
to
predict
the
ratings
of
the
movies
the
customers
didn’t
watch.
Accordingly,
by
ranking
the
predictions
from
5
down
to
1,
your
Deep
Learning
model
will
be
able
to
recommend
which
movies
each
user
should
watch.
Creating
such
a
powerful
Recommender
System
is
quite
a
challenge
so
we
will
give
ourselves
two
shots.
Meaning
we
will
build
it
with
two
different
Deep
Learning
models.

Our
first
model
will
be
Deep
Belief
Networks,
complex
Boltzmann
Machines
that
will
be
covered
in
Part
5.
Then
our
second
model
will
be
with
the
powerful
AutoEncoders,
my
personal
favorites.
You
will
appreciate
the
contrast
between
their
simplicity,
and
what
they
are
capable
of.

And
you
will
even
be
able
to
apply
it
to
yourself
or
your
friends.
The
list
of
movies
will
be
explicit
so
you
will
simply
need
to
rate
the
movies
you
already
watched,
input
your
ratings
in
the
dataset,
execute
your
model
and
voila!
The
Recommender
System
will
tell
you
exactly
which
movies
you
would
love
one
night
you
if
are
out
of
ideas
of
what
to
watch
on
Netflix!

— Summary —

In
conclusion,
this
is
an
exciting
training
program
filled
with
intuition
tutorials,
practical
exercises
and
real-World
case
studies.

We
are
super
enthusiastic
about
Deep
Learning
and
hope
to
see
you
inside
the
class!

Kirill
& Hadelin

Who
is
the
target
audience?

  • Anyone
    interested
    in
    Deep
    Learning
  • Students
    who
    have
    at
    least
    high
    school
    knowledge
    in
    math
    and
    who
    want
    to
    start
    learning
    Deep
    Learning
  • Any
    intermediate
    level
    people
    who
    know
    the
    basics
    of
    Machine
    Learning
    or
    Deep
    Learning,
    including
    the
    classical
    algorithms
    like
    linear
    regression
    or
    logistic
    regression
    and
    more
    advanced
    topics
    like
    Artificial
    Neural
    Networks,
    but
    who
    want
    to
    learn
    more
    about
    it
    and
    explore
    all
    the
    different
    fields
    of
    Deep
    Learning
  • Anyone
    who
    is
    not
    that
    comfortable
    with
    coding
    but
    who
    is
    interested
    in
    Deep
    Learning
    and
    wants
    to
    apply
    it
    easily
    on
    datasets
  • Any
    students
    in
    college
    who
    want
    to
    start
    a
    career
    in
    Data
    Science
  • Any
    data
    analysts
    who
    want
    to
    level
    up
    in
    Deep
    Learning
  • Any
    people
    who
    are
    not
    satisfied
    with
    their
    job
    and
    who
    want
    to
    become
    a
    Data
    Scientist
  • Any
    people
    who
    want
    to
    create
    added
    value
    to
    their
    business
    by
    using
    powerful
    Deep
    Learning
    tools
  • Any
    business
    owners
    who
    want
    to
    understand
    how
    to
    leverage
    the
    Exponential
    technology
    of
    Deep
    Learning
    in
    their
    business
  • Any
    Entrepreneur
    who
    wants
    to
    create
    disruption
    in
    an
    industry
    using
    the
    most
    cutting
    edge
    Deep
    Learning
    algorithms

Credits: Jayadev
& monoj
kashayp
<<<
Thank
you
so
much
for
your
support
and
valuable
contribution!!!
🙂

Size:
3.25G

 

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