Machine Learning and Deep Learning on a trend!
All of a sudden, everybody is talking about them irrespective of whether people are understanding the dissimilarity or not. Whether you are a follower of data driven science or not- you probably would have heard these terms.
Let’s start discussing and differentiating with a classic example of cats vs. dogs. In the picture below, can you identify cat and dog? Over the time, you have already seen many cats and dogs; and so, you are capable of identifying them as well.
Also, we can not deny the fact that sometimes even humans make mistakes. So, expecting a computer to make mistakes is just normal.
For having a computer, do identify and classify with the help of a standard machine learning approach, we would manually have to select the relevant features of an image such as the corners or the edges in order get the machine learning model get trained. The model then references those image in accordance to identify and classify new objects.
Here is an example of object recognition. Moreover, these can also be used for object and scene detection.
If you are stuck with making a decision whether to choose machine learning or deep learning, start questing yourself first if you have a high-performance GPU and lots of labeled data. If you don’t have any of these things you can better try your luck with machine learning. As deep learning is generally more complex and it will take you at least a few thousand images for driving the desired results.
Moreover, you’ll need a high-performance GPU so that it can identify images with more precision and that too in lesser time.
On the other hand, if you are choosing machine learning, you have an exclusive option of training your model in many different classifiers and you may also know which features to extract that will get the most reliable results.
With machine learning, you have an option of using a combination of approaches. You can also use MATLAB for quickly trying these combinations.
As already mentioned, machine learning requires fewer data whereas deep learning requires huge! The advantage is that you can get a trained model that is faster as well 😉