Machine learning Vs Deep learning

1. 11/4/2019 Machine learning Vs Deep learning
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Machine learning and deep learning are two subsets of artificial intelligence that have
received a lot of attention over the past two years. If you are looking to understand the two
terms here in the simplest possible way, there is no better place than this.
So if you've been with me for a while, I'll try to explain what's the difference between deep
learning and machine learning, and how you can leverage these two subsets of AI for new
and exciting business opportunities.
Deep Learning vs Machine Learning

2. 11/4/2019 Machine learning Vs Deep learning
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Before I get started, I hope you have a basic understanding of what the terms deep learning
and machine learning mean. If you don't, here are some general definitions of deep learning
and machine learning for dummies:
Machine Learning for Dummies:
A subset of artificial intelligence associated with the creation of algorithms can modify itself
without human intervention to produce the desired product - by feeding through structured
data.
Deep learning for dummies:
A subset of machine learning, where algorithms are created and operated similar to machine
learning, but there are many layers of these algorithms — each providing a different
interpretation of the data it feeds. Such a network of algorithms are called artificial neural
networks, you name them because their performance is an impulse; An attempt to mimic the
function of human neural networks in the brain.
I've tried to put those definitions in the simplest way possible, but if this doesn't help you
make any difference, here's an example.
Look at the picture above. What you see is a collection of pictures of cats and dogs. Now, you
want to isolate images of dogs and cats with the help of machine learning algorithms and
deep learning networks.
Deep Learning vs. Machine Learning Basics:
When the problem is solved by machine learning:
To help classify the images in the collection according to the two categories of ML algorithm
dogs and cats, you need to display these images collectively. What algorithm does it know?
The answer to this question, like the above definition of machine learning for dummies, is
structured data. Label images of dogs and cats as you define specific characteristics of
animals. The data is sufficient to learn the machine learning algorithm, and then it works on

3. 11/4/2019 Machine learning Vs Deep learning
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the labels it understands and classifies millions of other images of two animals according to
the properties learned by the labels mentioned.
Deep Learning vs. Machine Learning:
When the Problem is Solved by Deep Learning:
Deep learning networks take a different approach to addressing this issue. The main
advantage of deep learning networks is that there is no need for structured / labeled data of
images to classify the two animals. Using deep learning, artificial neural networks send input
(image data) through different layers of the network, each network periodically defining the
specific properties of the images. This is similar to how our human brain works to solve
problems - by sending questions through different series of concepts and related questions to
find answers.
Once the data is processed through layers in deep neural networks, the system will find
enough identifiers to classify the two animals from their images.
This is an example for you to understand the differences between machine learning basics
and how deep learning networks work. Both deep learning and machine learning do not
actually apply to many situations at once. The reason for this will be explained after you read
it.
So in that example, we saw that machine learning algorithm needed labeled / structured data
to understand the differences between images of cats and dogs, learn classification, and then
produce.
On the other hand, the deep learning network was able to classify images of two animals
through the data processed in the layers of the network. This does not require labeled /
unstructured data as it is based on the different outputs processed by each layer, which
creates a unified way to classify the images.
1. The main difference between Deep Learning vs. Machine Learning arises from the way
data is delivered to the system. Machine learning algorithms always require structured
data, but deep learning networks rely on ANN (artificial neural networks) layers.

4. 11/4/2019 Machine learning Vs Deep learning
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2. Machine learning algorithms are built to “learn” to do things by understanding labeled
data, and then use it to produce more results with more data sets. However, they must
be re-trained by human intervention when the original product is not desired.
3. Deep learning networks do not require human intervention, as they are grouped by
different layers of concepts in neural networks and eventually learn through their own
shortcomings. However, if the quality of the data is insufficient, they are also subject to
defective results.
4. Data is governor here. It is the quality of the data that ultimately determines the quality
of the outcome.
What we haven't seen in the example, but the important things to note are:
1. Because machine learning algorithms require labeled data, they are not suitable for
solving complex queries that involve large amounts of data.
2. In this case, however, we have seen the application of deep learning networks to solve
such a small question. The actual application of deep learning neural networks is very
large. In fact, considering the number of layers, hierarchies and concepts that these
networks process, they are only sufficient to perform complex calculations rather than
simple ones.
3. These two subsets of AI actually revolve around data to provide some sort of
“intelligence”. However, deep machine learning requires more data than a traditional
machine learning algorithm. This is because it can only detect edges (concepts,
differences) in the layers of neural networks when exposed to a million data points. On
the other hand, machine learning algorithms can learn by pre-programmed defined
criteria.
So with that example and the explanation of deep learning vs machine learning basics, I hope
you understand the differences between the two. Since these are Lehman descriptions, my
attempt is to avoid introducing mostly non-technical terms for those who want to impact the
development of AI and machine learning for their business.
Now is the time to hammer the final nail. When should you use Deep Learning or Machine
Learning in your business?
When should deep learning be used?
If you are a company with a data boat load of comments.

5. 11/4/2019 Machine learning Vs Deep learning
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Artificial Intelligence
If you need to solve some of the most difficult problems in machine learning.
If you can spend a lot of computing resources and costs to run hardware and software
to train deep learning networks.
When to use machine learning development for your business?
If you have structured and usable data to train machine learning algorithms.
If you are looking to take advantage of AI before the competition.
Best machine learning solutions help in the automation of various business operations,
including identity verification, advertising, marketing, and data collection, and help to
create great opportunities for the future.
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