DEEP
LEARNING
“ To understand Deep Learning, you should have a crystal clear idea of Machine
Learning. Machine Learning is one of the key subfields in the universal set of Artificial
Intelligence. If someone asks what Machine Learning (ML) is, what would
the perfect answer be? The common idea of the majority is that it is just a bunch
of if-else statements, which is not correct. ML is the study of algorithms that try
to learn from the given data which will be used to predict future scenarios for
new data or instances. There are many traditional learning approaches such as
linear regression, logistic regression, SVMs, etc. But deep learning has become
the trending approach these days due to various reasons.
”
Coming back to our question, what is deep learning? It can also be identified as a Machine Learning
technique, but it has been inspired by the architecture of the human brain, which is made up
of millions of neurons. The basis of deep learning is, creating artificial neurons using algorithms.
These advanced algorithms are not new to this century. Due to the low processing power in the
computers and lack of data, these algorithms have been ignored for a long time. But the transition
from these traditional computers towards the modern GPUs and data collection techniques operated
over the internet, boosted Deep Learning to the next level.
How does this deep learning really work? That’s a tough question to answer. Let me slowly unpack
this black box for you. I will answer it using a supervised learning problem approach where we have
labeled data. To use these DL (Deep Learning) methods what we need is a big data set. And you
have to pre-process this data which can be used to feed our Artificial Neural Networks (ANN). Then,
you can design your neural network architecture. ‘Keras’ is one of the famous libraries (which is my
personal favorite) that can be used to work with DL that runs on top of TensorFlow.
GAUGE Magazine
University of Peradeniya
Page
57