deep learning and machine learning both offer ways to train models and classify
data this video compares the two and it offers ways to help you decide which one
to use let's start by discussing the classic example of cats versus dogs now
in this picture do you see a cat or a dog how are you
able to answer that chances are you've seen many cats and dogs over time and so
you've learned how to identify them this is essentially what we're trying to get
a computer to do learn from and recognize examples also keep in mind
that sometimes even humans can get identification wrong so we might expect
a computer to make similar errors to have a computer do classification using
a standard machine learning approach we'd manually select the relevant
features of an image such as edges or corners in order to train the machine
learning model the model then references those features when analyzing and
classifying new objects this is an example of object recognition
however these techniques can also be used for scene recognition and object
detection when solving a machine learning problem you follow a specific
workflow you start with an image and then you extract relevant features from
it then you create a model that describes or predicts the object on the
other hand with deep learning you skip the manual step of extracting features
from images instead you feed images directly into the deep learning
algorithm which then predicts the object so deep learning is a subtype of machine
learning it deals directly with images and is often more complex for the rest
of the video when I mention machine learning I mean anything not in the deep
learning category when choosing between machine learning and deep learning you
should ask yourself whether you have a high-performance GPU and lots of label
data if you don't have either of these things you'll have better luck using
machine learning over deep learning this is because deep learning is generally
more complex so you'll need at least a few thousand images to get reliable
results you'll also need a high performance GPU so the model spends less
time analyzing those images if you choose machine learning you
have the option to train your model in many different classifiers you may also
know which features to extract that will produce the best results plus with
machine learning you have the flexibility to choose a combination of
approaches use different classifiers and features to see which arrangement works
best for your data you can use MATLAB to try these combinations quickly also keep
in mind that if you are looking to do things like base detection you can use
out-of-the-box MATLAB examples as I mentioned before you need less data with
machine learning than with deep learning and you can get to a trained model
faster too however deep learning has become very popular recently because it
is highly accurate you don't have to understand which features are the best
representation of the object these are learned for you but in a deep
learning model you need a large amount of data which means the model can take a
long time to train you are also responsible for many of the parameters
and because the model is a black box if something isn't working correctly it may
be hard to debug so in summary the choice between machine learning and deep
learning depends on your data and the problem you're trying to solve MATLAB
can help you with both of these techniques either separately or as a
combined approach to find out more visit mathworks comm slash deep learning
you