What are the differences between machine learning and deep learning

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What are the differences between Machine Learning
and Deep Learning?
The technological marvel, Artificial Intelligence, has evolved significantly
to give rise to two other ingenious technologies — Machine learning and
Deep Learning. Both of these technologies have created a buzz in the
software market and are setting new trends by executing unthinkable
tasks. ML and DL are opening up new avenues for new-age entrepreneurs
by making way for intelligent and intuitive software solutions.
Entrepreneurs, these days, are roping in a Machine Learning
Company for designing disruptive solutions for them.

Although Machine Learning and Deep Learning are subsets of the same
technology — Artificial intelligence — they are quite different from each
other. And, new-age businesses planning to leverage the technical
benefits of these amazing technologies, must understand their differences
well, so that they are able to implement these technologies correctly.
This post provides deep insights into Machine Learning and Deep
Learning and explores their differences.
Machine Learning: An Overview
Machine Learning is a subset of Artificial Intelligence. It provides a
system with the capacity to learn as well as improve from the experience
gained, without the need for being programmed to that level. Data is
employed for training and then finding the correct outcome. Machine
Learning solutions perform a function using the data fed to it and
progressively improve with time.
This technology is used for executing all types of automated tasks across
several industrial domains right from data security companies for
identifying malware to finance businesses who want to receive alerts for
favorable trades.
Machine Learning is classified into 3 categories

Supervised Learning: This approach involves a wholly governed
learning process, wherein the result is predicted based on a set of training
samples provided with training labels also called the classifying data
point. Here machine learning developers tell the algorithm what to
predict during the training time, hence the name supervised learning.
Unsupervised Learning: This approach does not get training labels
for the training samples. Here, the algorithms are created in such a
manner that they are capable of finding suitable patterns and structures
within the data provided. Similar data points are assembled together after
the consistent patterns become apparent. Various data point appears in
different clusters. It projects high-dimensional data into low-dimensional
ones, for visualizing or analyzing.
Reinforced Learning: This approach involves a robot-like agent that
performs actions and quantifies outcomes to learn how it should behave
within a given environment. It follows the MDP (Markov Decision
Process) — receives a reward point for making a correct response. This
expedites the confidence level of the agent and encourages it to take up
more such functions.
Example:

When ML is applied to an on-demand music streaming service, its task is
to find out what new songs/artists to suggest to specific groups of
listeners. For making decisions about such recommendations, an ML
algorithm relates the user’s preferences with those of other users with
similar musical tastes.
Deep Learning: An Overview
Deep learning, a subset of ML, is a technology where recurrent neural
network and artificial neural network comes together. The formation of
algorithms is quite similar to that of ML, only with the difference that
there are more algorithms levels involved. All of these networks combine
to form a layered structure of algorithms termed the artificial neural
network — it’s just like the biological network of neurons present inside a
human brain. Deep learning solutions continuously analyze data with a
logical structure, just like the processing that happens inside a human
brain to draw conclusions.
Deep Learning applications can solve complicated problems by
processing the algorithms and is way more capable than the standard ML
models.
Multiple layers that are stacked between the input and output
layer

• Input layer consisting of a time series data or pixels of an image
• Hidden Layer called weights; it’s learned while the neural
network is being trained
• The output layer is the final layer that provides a predictive
analysis based on the input that has been fed into the network.
Example:
The Google-developed gaming app named AlphaGo is a perfect example
of Deep Learning implementation. A computer program has been created
using a neural network for playing this abstract board game against
professional players. And, AlphaGo has successfully defeated world-
famous players of the Go game — an instance of artificial intelligence
defeating human intelligence.
Deep learning is also used for functions like translation, speech
recognition, and operating self-driving cars.
Key Differences between Machine Learning and Deep Learning

Let’s now explore the key differences between Machine Learning and
Deep Learning based on the following parameters.
Basic Functioning Principle
Machine learning is a super-set of Deep learning that takes in data as an
input, then parses the data and makes decisions based on the learning
while being trained. Deep learning, on the other hand, is a subset of ML,
here data is accepted as an input for making intelligent and intuitive
decisions using a layer-wise stacked artificial neural network.

Machine learning solutions are apt for solving problems that are simple
or partly complex; whereas Deep Learning models are suitable for solving
more complex problems.
The Type of Data involved and the Problem Solving Technique
Machine learning solutions usually deal with structured data and hence,
employ traditional algorithms such as linear regression. Deep learning
models can work with structured as well as unstructured data as they
depend on the layers of an artificial neural network. Machine Learning
algorithms parse data in parts and after processing these parts separately,
integrate them to produce the final outcome. Contrarily, Deep learning
systems follow an end-to-end approach — take in the input for a problem
and produces the end-result directly.
For example, a program has to identify specific objects — license plates of
cars parked in a lot — within an image; find out the objects’ identity and
location. With an ML solution, this task will be executed in two steps —
detecting the object and then recognizing it. Using a Deep Learning
application, the task will be completed at one go — you input the image
and the identified objects along with their location appear in a single
result.
Data Dependencies and Output

Machine Learning handles thousands of data points and its outputs
include numerical values or classifications. Deep learning, on the other
hand, handles millions of data and its outputs range from numerical
values to free-form elements like text and speech.
ML depends on a large amount of data, yet can function smoothly with a
smaller amount of data as well. But this is not the case with deep learning
models — they perform well only if humongous data is fed to them.
Algorithm Usage
ML employs different kinds of automated algorithms for parsing data and
turns them into model functions for predicting future actions or making
informed decisions based on the learning acquired from collected and
processed data. Data analysts detect these algorithms for examining
particular variables within sets of data.
Deep Learning structures the algorithms in layers to build an artificial
neural network. With this approach, data passes through several
processing layers for interpreting data features and relations. This neural
network is capable of learning and then forming intelligent decisions on
its own.
Hardware Requirement

ML programs are less likely to be complex as compared to deep learning
algorithms. Machine learning programs need a CPU to process and so,
can function on conventional computers or low-end machines without the
need for high computing power. Deep learning algorithms, on the other
hand, require way more powerful hardware as well as resources; because
of the complex nature of the mathematical calculations involved and the
need for processing a huge amount of data. They use hardware like GPUs
or graphical processing units, and this increases the demand for power.
GPUs possess high bandwidth memory and hide latency while
transferring memory on account of thread parallelism.
Feature Extraction Methodology
The Deep learning mechanism is an ideal way of extracting meaningful
functions out of raw data and is not dependant on hand-crafted features
such as a histogram of gradients, binary patterns, etc. Moreover, the
feature extraction methodology is hierarchical — features are learned
layer-wise. As a result, it learns low-level features from the initial layers
and as it goes up the hierarchy, more abstract data representation is
learned.
However, ML is not a suitable option when there is a need to extract
meaningful features from data. This is because, for good performance, it
is highly dependent on hand-crafted features provided as input.

The degree of human intervention needed
ML needs continuous human intervention for obtaining the best results.
Deep learning does involve a more complex set-up procedure, but once
set up requires very less human intervention.
Execution Time involved
Machine Learning algorithms consume much lesser time for training the
model, but testing the model is time-consuming. On the contrary, Deep
learning applications take much lesser time to test the model but take a
bit longer to train the model.
Industry Readiness
It’s easy to decode ML algorithms and it can interpret which parameters
were picked and why those parameters were chosen. Deep learning
algorithms, on the contrary, are simply a blackbox and are capable of
outshining humans in regards to performance. Thus, ML solutions are
better bait for industry application as compared to Deep learning
solutions.
Final Verdict

Machine Learning and Deep Learning are here to stay. Both of these
technologies possess a huge potential in transforming every industry
vertical. Dangerous tasks such as working within harsh eco-systems,
activities concerning space travel, etc. are expected to be replaced by ML
and DL models in the near future. So it’s high time to be well versed with
these outstanding technologies.
However, developing and implementing ML and DL solutions is no
cakewalk and so, it’s advisable to hire experienced professionals for this
purpose. For technical assistance in designing, deploying, and
maintaining, ML/DL models, Biz4Solutions, a highly experienced and
competent outsourcing software company in India, would be a
good choice. We have extensive experience and expertise in dealing with
ML and DL systems for global clients.
To know more about our core technologies, refer to links below
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