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pie torch an open source deep learning
framework used to build some of the
world's most famous artificial
intelligence products it was created at
The Meta AI research lab in 2016 but is
actually derived from the Lua based
torch library that dates back to 2002.
fundamentally it's a library for
programming with tensors which are
basically just multi-dimensional arrays
that represent data and parameters in
deep neural networks sounds complicated
but its focused on usability will have
you training machine learning models
with just a few lines of python in
addition it facilitates high performance
parallel Computing on a GPU thanks to
nvidia's Cuda platform developers love
prototyping with it because it supports
a dynamic computational graph allowing
models to be optimized at runtime it
does this by constructing a directed
acyclic graph consisting of functions
that keeps track of all the executed
operations on the tensors allowing you
to change the shape size and operations
after every iteration if needed pytorch
has been used to train models for
computer vision AI like Tesla autopilot
image generators like stable diffusion
and speech recognition models like open
AI whisper just to name a few to get
started install Pi torque and optionally
Cuda if you want to accelerate Computing
on your GPU now import it into a python
file or notebook like I mentioned a
tensor is similar to a multi-dimensional
array create a 2d array or Matrix with
python then use torch to convert it into
a tensor now we can run all kinds of
computations on it like we might convert
all these integers into random floating
points we can also perform linear
algebra by taking multiple tensors and
multiplying them together what you came
here to do though is build a deep neural
network like an image classifier to
handle that we can define a new class
that inherits from the neural network
module class inside the Constructor we
can build it out layer by layer the
flattened layer will take a
multi-dimensional input like an image
and convert it to one dimension from
there sequential is used to create a
container of layers that the data will
flow through each layer has multiple
nodes where each node is like its own
mini statistical model as each data
point flows through it it'll try to
guess the output and gradually update a
mapping of weights to determine in the
importance of a given variable linear is
a fully connected layer that takes the
flat and 28 by 28 image and transforms
it to an output of 512. this layer is
followed by a non-linear activation
function when activated it means that
feature might be important and outputs
the node otherwise it just outputs zero
and finally we finish with a fully
connected layer that outputs the 10
labels the model is trying to predict
with these pieces in place that next
step is to define a forward method that
describes the flow of data and now
instantiate the model to a GPU and pass
it some input data this will
automatically call its forward method
for training and prediction
congratulations you just built a neural
network this has been pytorch in 100
seconds thanks for watching and I will
see you in the next one
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