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>> We've now seen how cool deep learning can be.
We cannot apply it in a wide range of areas and
yet we need high accuracy by learning really detailed features of our data, and
how your networks can support that.
We've seen how it can be applied for
various tasks in image analysis in computer vision.
Let's now revisit the block diagram that we saw summarizing regression and
classification and other machinery tasks.
How can be applied here for computer vision for deep learning?
So in particular, let's talk about deep features for classifying images.
So deep features for image classification.
The input here are pairs of images with their labels.
So, the labels that we've looked at were things like, whether there is a cat,
the dog, a house or some other object in the image.
And now, we feed that through the feature extractor.
In this case, we're using a deep learning model as a feature extractor.
So the output here, what we call the Deep features for
this particular image for every image.
And now we feed in this images,
representative features through a machinery model.
Where we use a simple classifier like logistic regression, in here.
Say logistic regression as an example.
And the output is our predicted labels.
Predicted labels.
And so we're going to feed in our predicted labels y hat and
the true labels, y into our measure of quality.
So y and y hat, and the measure of quality depends on your task.
For this task we use classification accuracy.
And so the parameters
w hat are really the parameters of the weights of the logistic regressive.
So these are our weights on features.
And what the machinery algorithm is gonna do, is take the classification accuracy,
try to make a little better by changing those weights w hat and updating them.
We've now seen how deep learning can give you really cool and
exciting results for a various tasks in computer vision.
And we saw those applied both two classification and
to retrieving of new images using raw neural networks as well as deep features.
And our notebooks that we explored showed us that's
really easy to build such deep learning models and
actually apply to really cool machine learning tasks in computer vision.
Both in classification and image retrieval which allows me to find exactly the kind
of tools that I'm really excited about.
And with this you'll be able to build a really exciting intelligent application
that uses one of the most sought after techniques today in machine learning,
deep learning.
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