what's going on everybody and welcome to
a very exciting announcement and that is
the neural networks from scratch series
is finally upon us it has been probably
the most requested series since I did
the practical machine learning series
where we did all of those typical
classical machine learning algorithms
like K nearest neighbors were vector
machines clustering algorithms and so on
what we did was we did them from scratch
as well and people really wanted to have
neural networks from scratch but that's
of course a huge huge series that's way
more complicated than any of the other
machine learning algorithms so it's just
kind of been one of these things that
has been like yeah I really want to do
that but can we even get that done and
so for the last few months Daniel and I
have been working on doing just that
trying to figure out can we even get
this done and if so how do we do that
and by neural networks from scratch I
mean if we do it it would it would have
had to have been truly from scratch so
no third-party libraries at all so what
I'm here to tell you today is we have
done it it is possible and now it's kind
of being molded into an actual course
now two things they are first off we do
everything from scratch in Python but
that this gets extremely laborious in
terms of like putting everything finally
together so and it's kind of silly to
ignore numpy like numpy is the most
important library for python at all so
we're gonna show everything from scratch
truly in Python and then we do it in
numpy just because it would be honestly
a disservice to not use numpy plus you
get to learn a ton about numpy so again
everything is truly from scratch in
Python and then we show you how to do it
in numpy and then that's it as far as
how we're out with the only library
really that we're using with this so
anyway it's it's super super interesting
there's like there's truly no stone
unturned so if you want to learn how
neural networks actually work there's
really no better way than then doing it
this way so what I'm talking about is
truly coding every neuron you're coding
the activation file
so we're talking like sigmoid softmax
obviously rectified linear and in just a
linear activation so this means we can
do both classification and regression
and then coding in like calculating loss
with cross-entropy
and then for our optimizers we've got
obviously stochastic gradient descent
and also obviously Adam but as well as a
de grad and rmsprop so coding your
neurons creating layers activation
functions calculating loss optimizing
doing back propagation all of this is
covered it's a ton of material and just
in notes it it was it is about 200 pages
so that's just in notes as the write up
forms that's really long otherwise known
as a book so along with the free videos
and sample code as always that's not
changing along with that we're going to
release this in book form as well so the
the the topic and material honestly is
something that you are highly likely to
first first you're going to need
multiple sittings to digest it's too
much material that you couldn't even
physically do it in a day or a few days
it's gonna need multiple sittings and
then also having multiple mediums just
makes sense so some sort of text-based
version something you can take away and
go look at it somewhere else in a
different room and then obviously in
video form there's things that we can do
in video that we just can't do in text
form like showing like animations and
diagrams and stuff like that so while
diagrams obviously we could do in like a
book but animations wise especially when
we're doing some of these some concepts
like doing dot products or transpose and
that kind of stuff where it might be a
little confusing if I just tell you in
words how it's done so having stuff like
animations just make sense also what
we're going to do is put like QR codes
in the book so you could just scan the
QR code and see the animation and we'll
try to put this good of diagrams as
possible but like I said for things like
a transpose it kind of is very useful to
just see the animation of it happening
anyways so we're going to put it out
also
book form and there's a few benefits to
that so what I'm gonna do is the book
we're just going to crowdfund the book
now that's because this course is still
somewhere between six and eight months
out there's so much information here it
just it's going to take a very long time
to cover everything and make sure
everything connects correctly this isn't
really something that like even in the
Machine practical machine learning
series that kind of stuff was that
basically I release in almost all
tutorials I release in what I would call
draft form it's one pass right up push
it because I can always add comments to
the video I can always edit the tutorial
it's really not that big of a deal in
the case of a book obviously I can't do
that but also this topic in general is
it's very complicated and I feel like if
it's not done correctly right out of the
gate it's gonna be very cumbersome to
anyone who's trying to learn all this
stuff if later we've got to release some
update to the book and be like oh by the
way here you go change it does it just
couldn't work like that so this is
something that's gonna take a lot more
polishing than what I typically do so
because it's gonna take that long what
we can do is I can share like the draft
version basically of the book so if
you're going to if you become a backer
there's going to be a link to the
Kickstarter page in the description what
we can do for you is offer a an access
to the actual draft of the book now what
that allows us to also do is allow
anyone who's reading that to like
highlight and post comments so if you're
confused you've got a question you think
something's wrong whatever it is you can
highlight that post a comment there's a
draft out right now as I'm recording
this from creating neurons to activation
functions and calculating the loss and
that's gonna be continually updated so
really it kind of lets you treat this
more like a course or something so you
as you go through it you can if you've
got any sort of pain point or whatever
you can post that comment and we can
actually help you and for us as we're
developing this this the series it helps
us to see okay maybe we should change
this or we should add in some more
information here and we can kind of see
where people are having a hard time that
way before the course
was public before I film on these videos
before you actually publish a book we
can get all those things right so that's
the plan a lot of information but
honestly this is this is probably the
coolest course I think by far ever to
date that will come out so I'm super
excited for it it is it's a lot of
material I my expectation is the book
will be somewhere between 300 and 500
pages it's just so much material and but
it's but it's super useful so you know
why might you want to do a neural
network from scratch just just from you
know I think for some people it's just
obvious or they're just genuinely
interested but for the rest of us you
know like why why this why would you do
this so if you're like me the path I
took to learning deep learning started
with tensorflow
widget boom jumped into tensorflow where
the optimizer is coded for me loss is
coded for me like I just import things
for this the layers I just import the
layer and that's super that's totally
fine if you're trying to solve an
already solved problem and I think for
many people and when they're going
through tutorials and this was me to it
you know where I'm learning about things
and I'm solving things but it's like a
pre solved problem it's a pre solved
data set it's a pre solved neural
network that's that is being given to me
that I already know is going to work and
when I've done it it's the same thing
right we're because that's kind of the
job of the educator we simplify the
problem so you can learn this this this
this pretty complex topic but when you
go out into the real world and start
trying to apply neural networks to
problems that have not yet been solved
it becomes very hard and doing things
like even just like a custom loss
function like what is lost how do I do
that how could i code that into my
neural network which optimizer should
you always use which activation function
should you always use different problems
need different activation functions and
then you start wondering you know you
might you can memorize in some cases
like okay I'm gonna use tan H here I'm
going to use rectified linear for
everything else I'm going to use maybe a
sigmoid here softmax here that kind of
thing you
you can sort of memorize like in this
case I could do this but you're you
don't really fully understand and when
you have problems like exploding
gradients or other things that maybe you
can diagnose but you have no idea how to
fix and if you understand how this stuff
actually works you can quite literally
pop the hood and fix it and figure out
how can I go about doing this
also just solving like like doing things
like reinforcement learning and some of
these other less like if you're just not
classic if you're classifying images but
you really probably don't need this but
if you're doing other things like
everything that people wanted me to do a
tutorial on the audio right the
text-to-speech there's so much custom
stuff going on there that without a
solid baseline of information first I
can't even begin to have a tutorial
series on that
same thing with a lot of these
reinforcement learning topics without a
heavy base on what we're doing why we're
doing that I can't do it the chat BOTS
for example that's another one people
want much more in-depth series well I
can't really do that because we're a
custom loss function is exactly what we
want to do there right so all of these
things kind of culminate into I really
want this series to happen but it's such
a huge series that it's just going to
take a lot of time but I think this is
the best way for me to do it like I said
everything free as as you you know and
love but we're also going to release the
book form and if you do crowdfund that
book you can get access to the drafts
make comments kind of follow along if
you really want to learn this stuff I
can't I just can't imagine a better way
than doing it this way especially with
the ability to just post comments right
you just highlight the spot right where
you're confused that's got to be the way
so anyways I'm super excited about this
I hope you guys are too I think this is
possibly a new way of doing way more in
depth stuff on my channel I feel like
I've always kind of been like one step
beyond basics but I could never get
really deep into things and I'm hoping
this could be a way that I can make that
happen so anyways you know basically
after this book there could be you know
the
doing things like chatbots
text-to-speech that kind of like way
more interesting but much larger
concepts in general so anyways I'm
really excited to see how this pans out
again there's a link in the description
for the Kickstarter and yeah questions
comments whatever feel free to leave
them below otherwise I'm really looking
forward to this see you in the next
video