Deep Learning with Tensorflow - The MNIST Database Video Lecture - Tensorflow

hello and welcome in this video we will
introduce you to the M NIST database
which contains a large set of
handwritten digits this database is a
great tool for experimenting with deep
learning models but before we get into
the structure of M NIST let's briefly
cover the main ideas behind deep warning
deep learning is a branch of machine
learning that uses algorithms to model
high-level abstractions in data
typically this involves the use of
multiple processing layers complex
structures and multiple nonlinear
transformations the main thing to note
is that since our brains use a large
interconnected structure a machine
should do the same to achieve better
results in this context the term deep
refers to a neural network with a large
number of hidden layers regardless of
the type of layers that are used
research papers have shown that deep
neural network architectures can achieve
better results than their shallow
counterparts in our case the term
learning refers to the process of
supervised learning for the handwritten
digit problem we start with a set of
images that are labeled with the known
correct digit each image is broken down
into pixels and these pixel intensities
form the images features the network
goes through a training process in order
to build a function that will map the
inputs to the desired outputs in other
words it needs to learn how to map a set
of pixels that describe an image into a
digit from 0 to 9 this training process
is performed using the previously
labeled images and it continues until
the network achieves a desired level of
accuracy according to Yamla Koons
website M NIST is a database of
handwritten digits with 60,000 samples
for training and 10,000 samples for
testing the database is a subset of a
larger set available from NIST
the authors have invested a lot of time
on the data set with normalization and
centering operations so each digit is
centered in a fixed size image we're
going to import the M NIST data set
using tensorflow but keep in mind that
this is a highly optimized data set as a
result you'll need to build your own
code if you actually want to visualize
the digits the one hot equals true
argument means that the label for each
digit will have one distinct bit turned
on for example 5 and 0 in standard
binary would look like this but using
the single bit one hot vector
representation they would look like this
the imported data can be broken up into
a few distinct subsets the training set
is used throughout the learning process
so for example when the neural network
analyzes a digit it can observe the
corresponding label in order to improve
its predictions from the training data
we also reserved 5,000 data points for a
validation set the validation set is
used to generate model properties like
classification error for example it's
also used to determine the optimal
number of hidden units or the stopping
point of the backpropagation algorithm
the ten thousand point test set contains
data that the model never saw during
training it's used to evaluate the
models performance on unfamiliar data in
order to gauge how much the model truly
learned at this point you should have a
basic understanding of the structure of
the M NIST data set thank you for
watching this video to practice and
learn more go to the lab and run the
code for yourself