What is the difference between Machine Learning, Deep Learning and Artificial Intelligence ?

So let's start with artificial intelligence.

The term artificial intelligence was first coined in the year 1956. The concept is pretty old, but it has gained its popularity recently.But why well, the reason is earlier we had very small amount of data the data we had Was not enough to predict the accurate result, but now there's a tremendous increase in the amount of data statistics suggest that by 2020 the accumulated volume of data will increase from 4.4 zettabyte stew roughly around 44 zettabytes or 44 trillion GBs of data along with such enormous amount of data. Now, we have more advanced algorithm and high-end computing power and storage that can deal with such large amount of data as a result. It is expected that 70% of Enterprise will Implement ai over the next 12 months which is up from 40 percent in 2016 and 51 percent in 2017.

Just for your understanding what does AI well, it's nothing but a technique that enables the machine to act like humans by replicating the behavior and nature with AI it is possible for machine to learn from the experience. The machines are just the responses based on new input there by performing human-like tasks. Artificial intelligence can be trained to accomplish specific tasks by processing large amount of data and recognizing pattern in them. So as AI researchers, we should think of ourselves as humble brick makers whose job is to study how to build components example Parts is planners or learning algorithm or accept anything that someday someone and somewhere will integrate into the intelligent systems some of the examples of artificial intelligence from our day-to-day life our Apple series just playing computer Tesla self-driving car and many more these examples are based on deep learning and natural language processing.

Well, this was about what is AI and how it gains its hype.

So moving on ahead. Let's discuss about machine learning and see what it is and white pros of an introduced. Well Machine learning came into existence in the late 80s and the early 90s, but what were the issues with the people which made the machine learning come into existence? Let us discuss them one by one in the field of Statistics. The problem was how to efficiently train

large complex model in the field of computer science and artificial intelligence.

The problem was how to train more robust version of AI system while in the case of Neuroscience problem faced by the researchers was how to design operation model of the brain. So these are some of the issues which had the largest influence and led to the existence of the machine learning. Now this machine learning shifted its focus from the symbolic approaches. It had inherited from the AI and move towards the methods and model. It had borrowed from statistics and probability Theory.

So let's proceed and see what exactly is machine learning.

Well Machine learning is a subset of AI which The computer to act and make data-driven decisions to carry out a certain task. These programs are algorithms are designed in a way that they can learn and improve over time when exposed to new data.

Let's say you want to create a system which tells the expected weight of a person based on its side. The first thing you do is you collect the data. Let's see there is how your data looks

like now each point on the graph represent one data point to start with we can draw a simple line to predict the weight based on the height.

For example, a simple line W equal x minus hundred where W is waiting kgs and edges hide and centimeter this line can help us to make the prediction. Our main goal is to reduce the difference between the estimated value and the actual value. So in order to achieve it we

try to draw a straight line that fits through all these different points and minimize the error.

So our main goal is to minimize the error and make them as small as possible decreasing the error or the difference between In the actual value and estimated value increases the performance of the model further on the more data points. We collect the better.

So from the next time if we feed a new data, for example height of a person to the model, it would easily predict the data for you and it will tell you what has predicted weight could be.

I hope you got a clear understanding of machine learning.

Let's learn about deep learning.

Now what is deep learning?

You can consider deep learning model as a rocket engine and its fuel is its huge amount of data that we feed to these algorithms the concept of deep learning is not new, but recently it's hype as increase and deep learning is getting more attention. This field is a particular kind of machine learning that is inspired by the functionality of our brain cells called neurons which led to the concept of artificial neural network. It simply takes the data connection between all the artificial neurons and adjust them according to the data pattern more neurons are added at the size of the data is large it automatically features learning at multiple levels of abstraction.

Thereby allowing a system to learn complex function mapping without depending on any specific algorithm.

Let us discuss some of the example of deep learning and understand it in a better way. Let me start with a simple example and explain you how things happen at a conceptual level. Let us try and understand how you recognize a square from other shapes. The first thing you do is you check whether there are four lines associated with a figure or not simple concept, right? If yes, we further check if they are connected and closed again a few years. We finally check whether it is perpendicular and all its sides are equal, correct, if Fulfills. Yes, it is a square.

So let's move on and focus our discussion on machine learning and deep learning the easiest way to understand the difference between the machine learning and deep learning is to know that deep learning is machine learning more specifically.

Let's take few important parameter and compare machine learning with deep learning. So starting with data dependencies, the most important difference between deep learning and machine learning is its performance as the volume of the data gets increased from the below graph. You can see that when the size of the data is small deep learning algorithm doesn't perform that well, but why well, this is because deep learning algorithm needs a large amount of data to understand it perfectly on the other hand the machine learning algorithm can easily work with smaller data set fine.

Next comes the hardware dependencies deep learning. Are heavily dependent on high-end machines while the machine learning algorithm can work on low machines as well. This is because the requirement of deep learning algorithm include gpus which is an integral part of its working the Deep learning algorithm requires gpus as they do a large amount of matrix multiplication operations, and these operations can only be efficiently optimized using a GPU as it is built for this purpose.

I hope that things are getting clearer to you. So let's move on ahead and see the next parameter. So our next parameter is problem solving approach when we are solving a problem using traditional machine learning algorithm. It is generally recommended that we first break down the problem into different sub parts solve them individually and then finally combine them

to get the desired result. This is how the machine learning algorithm handles the problems on the other hand the Deep learning algorithm solves the problem

from end to end.

Happy learning!

Tags

• machine learning
• artificial intelligence
• deep learning