The document discusses the differences between machine learning and deep learning, highlighting that machine learning involves basic predictive algorithms and uses structured data, whereas deep learning utilizes complex neural networks to handle large, unstructured datasets. It outlines various neural network architectures, such as convolutional and recurrent networks, and their applications in fields like customer service, automotive industry, and speech recognition. Overall, deep learning is portrayed as an advanced subset of machine learning that offers enhanced capabilities for processing complex data.

1. Machine Learning vs Deep Learning: Here’s what you must know!

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Artificial Intelligence (AI) and Machine Learning (ML) are two words casually
thrown around in everyday conversations, be it at offices, institutes or technology
meetups. Artificial Intelligence is said to be the future enabled by Machine
Learning.
Now, Artificial Intelligence is defined as “the theory and development of computer
systems able to perform tasks normally requiring human intelligence, such as
visual perception, speech recognition, decision-making, and translation between
languages.” Putting it simply means making machines smarter to replicate human
tasks, and Machine Learning is the technique (using available data) to make this
possible.

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Researchers have been experimenting with frameworks to build algorithms,
which teach machines to deal with data just like humans do. These algorithms
lead to the formation of artificial neural networks that sample data to predict
near-accurate outcomes. To assist in building these artificial neural networks,
some companies have released open neural network libraries such as Google’s
Tensorflow (released in November 2015), among others, to build models that
process and predict application-specific cases. Tensorflow, for instance, runs on
GPUs, CPUs, desktop, server and mobile computing platforms. Some other
frameworks are Caffe, Deeplearning4j and Distributed Deep Learning. These
frameworks support languages such as Python, C/C++, and Java.

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It should be noted that artificial neural networks function just like a real brain that is
connected via neurons. So, each neuron processes data, which is then passed on to
the next neuron and so on, and the network keeps changing and adapting
accordingly. Now, for dealing with more complex data, machine learning has to be
derived from deep networks known as deep neural networks.
In our previous blogposts, we’ve discussed at length about Artificial Intelligence,
Machine Learning and Deep Learning, and how these terms cannot be interchanged,
though they sound similar. In this blogpost, we will discuss how Machine Learning is
different from Deep Learning.

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What factors differentiate Machine Learning from Deep Learning?
Machine Learning crunches data and tries to predict the desired outcome. The
neural networks formed are usually shallow and made of one input, one output, and
barely a hidden layer. Machine learning can be broadly classified into two types –
Supervised and Unsupervised. The former involves labelled data sets with specific
input and output, while the latter uses data sets with no specific structure.
On the other hand, now imagine the data that needs to be crunched is really
gigantic and the simulations are way too complex. This calls for a deeper
understanding or learning, which is made possible using complex layers. Deep
Learning networks are for far more complex problems and include a number of node
layers that indicate their depth.

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In our previous blogpost, we learnt about the four architectures of Deep Learning.
Let’s summarise them quickly:
Unsupervised Pre-trained Networks (UPNs)
Unlike traditional machine learning algorithms, deep learning networks can
perform automatic feature extraction without the need for human intervention. So,
unsupervised means without telling the network what is right or wrong, which it
will will figure out on its own. And, pre-trained means using a data set to train the
neural network. For example, training pairs of layers as Restricted Boltzmann
Machines. It will then use the trained weights for supervised training. However, this
method isn’t efficient to handle complex image processing tasks, which brings
Convolutions or Convolutional Neural Networks (CNNs) to the forefront.

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Convolutional Neural Networks (CNNs)
Convolutional Neural Networks use replicas of the same neuron, which means
neurons can be learnt and used at multiple places. This simplifies the process,
especially during object or image recognition. Convolutional neural
network architectures assume that the inputs are images. This allows encoding a
few properties into the architecture. It also reduces the number of parameters in
the network.

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Recurrent Neural Networks
Recurrent Neural Networks (RNN) use sequential information and do not assume
all inputs and outputs are independent like we see in traditional neural networks.
So, unlike feed-forward neural networks, RNNs can utilize their internal memory
to process sequence inputs. They rely on preceding computations and what has
been already calculated. It is applicable for tasks such as speech recognition,
handwriting recognition, or any similar unsegmented task.

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Recursive Neural Networks
A Recursive Neural Network is a generalisation of a Recurrent Neural Network and is
generated by applying a fixed and consistent set of weights repetitively, or
recursively, over the structure. Recursive Neural Networks take the form of a tree,
while Recurrent is a chain. Recursive Neural Nets have been utilized in Natural
Language Processing (NLP) for tasks such as Sentiment Analysis.
In a nutshell, Deep Learning is nothing but an advanced method of Machine
Learning. Deep Learning networks deal with unlabelled data, which is trained. Every
node in these deep layer learns the set of features automatically. It then aims to
reconstruct the input and tries to do so by minimizing the guesswork with each
passing node. It doesn’t need specific data and in fact is so smart that draws co-
relations from the feature set to get optimal results. They are capable of learning
gigantic data sets with numerous parameters, and form structures from unlabelled
or unstructured data.

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Now, let’s take a look the key differences:

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The future with Machine Learning and Deep Learning
Moving further, let’s take a look at the use cases of both Machine Learning and
Deep Learning. However, one should note that Machine Learning use cases are
available while Deep Learning are still in the developing stage.
While Machine Learning plays a huge role in Artificial Intelligence, it is the
possibilities introduced by Deep Learning that is changing the world as we know it.
These technologies will see a future in many industries, some of which are:

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Customer service
Machine Learning is being implemented to understand and answer customer
queries as accurately and soon as possible. For instance, it is very common to find a
chatbot on product websites, which is trained to answer all customer queries
related to the product and after services. Deep Learning takes it a step further by
gauging customer’s mood, interests and emotions (in real-time) and making
available dynamic content for a more refined customer service.

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Automotive industry
Autonomous cars have been hitting the headlines on and off. From Google to Uber,
everyone is trying their hand at it. Machine Learning and Deep Learning sit
comfortably at its core, but what’s even more interesting is the autonomous
customer care making CSRs more efficient with these new technologies. Digital
CSRs learn and offer information that is almost accurate and in shorter span of
time.

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Speech recognition
Machine Learning plays a huge role in speech recognition by learning from users
over the time. And, Deep Learning can go beyond the role played by Machine
Learning by introducing abilities to classify audio, recognise speakers, among other
things.
Deep Learning has all benefits of Machine Learning and is considered to become
the major driver towards Artificial Intelligence. Start-ups, MNCs, researchers and
government bodies have realised the potential of AI, and have begun tapping into
its potential to make our lives easier.

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Artificial Intelligence and Big Data are believed to the trends that one should
watch out for the future. Today, there are many courses available online that
offer real-time, comprehensive training in these newer, emerging technologies.

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