YouTube Transcript:
TensorFlow in 100 Seconds

tensorflow an open source machine
learning framework famous for powering
deep neural networks with high-level
code it was developed by the google
brain team and first released in 2015.
it's most commonly used with python but
can run in other languages like
javascript c plus plus and java at its
core it's just a library for programming
with linear algebra and statistics as
you know the word tensor describes a
multilinear relationship between sets of
algebraic objects within a vector space
aka a multi-dimensional array what makes
it special is its collection of apis for
data processing visualization model
evaluation and deployment that make deep
learning accessible to the average
developer it's extremely portable and is
able to run on tiny mobile cpus or
microcontrollers with tensorflow lite
can run in the browser with
tensorflow.js while the core library can
scale up to multiple gpus or run on
tensor processing units ships engineered
specifically to run tensorflow at a
massive scale it's used in medicine for
object detection and mri images by
twitter to sort your timeline by tweet
relevance by spotify to recommend music
by paypal for fraud detection in
addition to many other applications like
self-driving cars natural language
processing and so on to build your own
neural network right now create a python
file and install tensorflow next we'll
need some data like fashion mnist which
we can automatically import the goal is
to train a model that can predict the
clothing type of each image tensorflow
has a subclassing api for expert users
but also integrates with the
beginner-friendly keras library which
has a sequential api that can easily
build neural networks layer by layer we
start with a flattened layer that takes
the 28 by 28 pixel image as an input and
converts it into a one-dimensional array
this input layer is then fed into a
dense layer with 128 fully connected
neurons or nodes you can think of each
node like its own linear regression as
each data point flows through it it'll
try to guess the output and gradually
update a mapping of weights to determine
the importance of a given variable in
this case it uses a rectified linear
activation function that will output the
input if a certain threshold is met
otherwise it will just output zero and
the behavior of this layer can be
customized by tuning as hyperparameters
finally we have our output layer which
is also dense but is limited to 10 nodes
which corresponds to the total number of
clothing types in the data set now we
can compile the model and tell it to
optimize a certain loss function like
sparse categorical cross entropy as we
train the model for multiple epochs its
accuracy should gradually improve the
end result is a model that makes a
prediction with the likelihood that an
image is a certain type of clothing
congratulations you just built a neural
network this has been tensorflow in 100
seconds hit the like button if you want
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