These are the terms which have confused a lot of people and if you too are one of them, let me resolve it for you. Well artificial intelligence is an umbrella under which machine learning and deep learning comes. You can also see in the diagram that even deep learning is a subset of machine learning, so you can say that all three of them, AI, machine learning and deep learning are just the subset of each other.

ai vs ml

So let’s move on and understand how exactly they differ from each other. Let’s start with artificial intelligence.

Artificial Intelligence:

It’s nothing but a technique that enables the machine to act like humans by replicating behavior and nature. With AI it is possible for machines to learn from the experience. The machines are just the responses based on new input by performing human-like tasks. Artificial intelligence can be trained to accomplish specific tasks by processing large amounts of data and recognizing patterns in them.

You can consider that building an artificial intelligence is like Building a Church, the first church took generations to finish. So most of the workers working on it never saw the final outcome. Those working on it took pride in their craft building bricks and chiseling stone that was going to be placed into the great structure. So as AI researchers, we should think of ourselves as humble brick makers whose job is to study how to build components, example is planners or learning algorithms or accept anything that someday someone somewhere will integrate into the intelligent systems. Some of the examples of artificial intelligence from our day-to-day life are Apple’s Siri, 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 AI is and how it gains its hype. So moving on ahead. Let’s discuss about

Machine learning :

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

Let’s see an example of machine learning. Let’s say you want to create a system which tells the expected weight of a person based on its size. The first thing you do is you collect the data.

Let’s see

What Does your data look like? Now each point on the graph represents 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 weight in 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 the actual value and estimated value increasing the performance of the model further on with more data points. The more we collect the better qur model will become. We can also improve our model by adding more variables and creating different production lines for them. Once the line is created. 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 the predicted weight could be. I hope you got a clear understanding of machine learning. So moving on ahead. Let’s learn about deep learning.

Now what is deep learning?

You can consider a 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 its hype has increased 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 networks. It simply takes the data connection between all the artificial neurons and adjusts 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. You know, what no one actually knows what happens inside a neural network and why it works so well, so currently you can call it a black box. Let us discuss some examples of deep learning and understand it in a better way.

Let me start with a simple example and explain to 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 with a simple concept, right? If yes, we further check if they are connected and closed again after a few years. We finally check whether it is perpendicular and all its sides are equal, correct, if it Fulfills it is a square. Well, it is nothing but a nested hierarchy of Concepts what we did here was take a complex task of identifying a square case and break it into simpler tasks. Now this deep learning also does the same thing but at a larger scale, let’s take an example of a machine which recognizes the animal. The tasks of the machine is to recognize whether the given image is of a cat or a dog.

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