Deep Learning Vs Machine Learning: The ultimate guide

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As the name suggests, Machine learning and deep learning are related to AI. They are not different names given to it but different relatable terms. In this article, you will learn about Deep learning vs machine learning. Now you might wonder what is the difference between machine learning and deep learning. Artificial intelligence is the main field where Machine learning is its subfield. Eventually, Machine learning helps in the creation of algorithms and statistical models. It allows computers to learn and make predictions or decisions without being explicitly programmed. Further, Deep learning is a co-topic of machine learning. The structure and function of the human brain inspire it.

What is Artificial intelligence (AI)?

Our world is full of Artificial intelligence. Also, it is like a computer program that plays games. It can be like a complicated algorithm, as to develop a vaccine using the RNA structure of a virus. Initially, artificial intelligence takes the help of computer science and data to enable problem-solving in machines. Also, machine learning and deep learning are the subparts.

Eventually, it is the processing of human intelligence by machines like computer systems. Furthermore, special applications of AI include expert systems and natural language processing. It also includes speech recognition as well as machine vision. The art, as well as the science of making intelligent machines as well as computer programs, is called AI. Here computers are used to understand human intelligence. AI does not relate to methods that are biologically observable.”

In short, artificial intelligence uses computer science and robust datasets. Moreover, it helps in problem-solving. Also, it uses the elements of machine learning and deep learning, which work in parallel with artificial intelligence. These elements consist of AI algorithms which in turn contain expert systems. Also, it makes predictions based on input data.

Example of AI:

Deep Blue 

It is a system where one has to program manually, for a limited set of inputs. In addition, this system called  Deep Blue is a chess-playing computer. Also, it was able to beat a world chess champion in 1997. Eventually, it could take its next step based on vast options of possible moves and outcomes. The system is reactive. So, to improve the chess performances, programmers had to go in and add more features and possibilities.

Institute which offers AI courses:

• Henry Harvin
• Deloitte institute
• Vector AI institute

Some focus on What is machine learning?

In Machine learning, the computer systems are not programmed. But, they learn and adapt automatically from experience. With an AI, one can tell a machine how to react to various sets of instructions by hand-coding each “decision.” Furthermore, it is a part of artificial intelligence and growing technology. It allows machines perfectly to learn from past data. Also, it performs a given task automatically. Machine Learning allows computers to learn from their experiences on their own. They have different programs. Also, in this blog you will know about deep learning vs machine learning.

In machine learning, systems are trained by feeding them with large amounts of data. Eventually, it follows a set of rules called an algorithm to analyze and draw inferences from the data. As more and more data is fed into the machine, the better it becomes at performing a task or making a decision.  It uses statistical methods to improve performance and predict the output 

For example you might have seen the shopping applications serving suggestions after your each buy. From this, you now know what is machine learning. It learns automatically after your every input. Similarly the songs applications suggest songs on your last played track records. This is all machine learning. Also, the main applications of ML are Email spam filtering, product recommendations as well as online fraud detection.

Example of Machine learning:

IBM Watson

Watson is fed with thousands of question-and-answer pairs, as well as examples of correct responses. So, it was able to beat two Jeopardy champions in an exhibition match using machine learning. For every answer, the machine has the matching question. Programmers would correct the mistakes if there is something wrong with it. Furthermore, this allows the model to change its algorithms, or one can say learn from its mistakes. So in a matter of seconds, it could search millions of pages of information and gives possible answers.

Institutes which offer Machine learning Courses:

• Henry Harvin
• Fireblaze AI school
• Marsian Technologies

What is Deep Learning?

Machine learning algorithms need human correction when they get something wrong. Whereas, deep learning algorithms gives better result through repeated cycles, without human intervention. A machine learning algorithm works from relatively small sets of data. But a deep learning algorithm works on big data sets. Also, they might include diverse and unstructured data. Deep learning is an upgraded version as well as a part of machine learning. It is a different kind of machine learning that works approximately in the same way but has the scope for different capabilities and methods. Also, it follows the functionality of human brain cells called neurons. This leads to the concept of artificial neural networks.

As it is also called deep neural learning, so from there we get this name called deep learning. Deep learning vs machine learning will clear all the doubts further. In this method, models use different layers to learn and discover insights from the data. The main applications of deep learning are self-driving cars, language translation, natural language processing, etc. You will know what is deep learning from the following models.

Some popular deep-learning models are

• Convolutional Neural Networks
• Recurrent Neural Networks
• Autoencoders
• Classic Neural Networks

Types of deep learning algorithms:

Eventually, there are a growing number of deep learning algorithms. They make these new goals reachable. We’ll cover two here just to illustrate deep learning vs machine learning. Also, there are other ways that data scientists and engineers are going to apply deep learning in the field.

Convolutional Neural Networks

Convolutional neural networks are specifically built algorithms. They are designed to work with images. It is the process that applies a heavy filter across every element of an image. Also, it helps the computer to understand and react to the small things within the picture itself.

Recurrent Neural Networks

Further, recurrent neural networks provide a main element of machine learning. That element is absent in simpler algorithms called memory. The computer retains past data points and decisions in mind. Furthermore, it considers them when revising current data or initialising the power of context.

Due to this, recurrent neural networks are considered as the major focus for natural language processing work. 

Example of Deep Learning:

AlphaGo

If you want to know deep learning vs machine learning in a better way then go through this example. AlphaGo is the first program. It won the Go World championship in 2015. And, it is a Chinese game. Also, it is famous for its complex strategy. It is more complicated than chess, with 10 to the power of 170 possible configurations on the board.

So, several games of Go were introduced to feed the mechanics into it. It played different versions of itself thousands of times. After learning from its mistakes after each game, AlphaGo became very good in its inventive moves. Furthermore, the latest version of the AlphaGo algorithm is MuZero.

Institutes which offer deep learning courses:

• Deep learning institutes of India
• Seven mentors

Detailed view on Deep learning vs machine learning:

Machine learning	Deep learning
It is an evolution of AI	It is a refined form of Machine Learning.
The Machine learning models are suitable for solving simple as well as bit-complex problems	Deep Learning solves complex machine-learning issues.
You can provide training using the CPU	Give training with the help of a dedicated GPU
The data represented in Machine Learning is quite different because it uses structured data	Here, the data used is quite different because it uses neural networks(ANN).
It is a subset of AI	It is a subset of machine learning
Machine learning contains training algorithms. Also, it identifies patterns and relationships in data.	Deep learning uses complex neural networks. It has many levels to analyze more complicated patterns
It Makes simple, linear correlations	It makes non-linear, complex correlations
Moreover, it can be trained on smaller data sets	They require large amounts of data set
It can work on low-end machines	They need high-end machines
Needs shorter training and lower accuracy	Needs longer training and higher accuracy
Here humans perform the feature engineering	Feature engineering is not needed here because important features are automatically detected by neural networks
The results of this model are easy to explain	The results of this model are difficult to explain
Machine learning algorithms include simple linear models as well as complex models. Examples are decision trees	This model works with artificial neural networks. Also, it consists of multiple layers and nodes
Banks, mailboxes, as well as hospitals all use machine learning	Deep learning technology uses decent algorithms, such as self-driving vehicles or surgical robots.

How does Machine Learning work?

If you still wonder what is machine learning, then go through this section. The working of machine learning models can be understood by the examples of deep learning vs machine learning. Identify the image of a living thing like a flower as well as a non-living thing like a vase. To identify this, the ML model takes images of flowers and vases as input.

Further, it derives the different features of images such as water content, material, stiffness, degradable or non-degradable etc. and applies the classification algorithm, and predicts the output. 

Take a look at the below image:

Some light on how Deep Learning Works?

We can understand what deep learning is with the same example of classifying flowers and vases. The deep learning vs machine learning model takes the images as the input.

Further, it feeds the information directly to the algorithms without requiring any manual feature extraction step. The images then go to the different layers of the artificial neural network and predict the final output.

Have a look at the below image:

The Future of machine learning and deep learning

Now that you know what is the difference between machine learning and deep learning then let’s see what is in the near future. Our future generations will be affected by Machines and deep learning. Also, it will affect our lives for generations to come. Eventually, every industry will be transformed by their capabilities. Curios jobs like going to space as well as working environments in harsh areas will be replaced with machines. The entertainment industry will also be affected. Rich new entertainment like science fiction will be enhanced.

Conclusion:

At last, we can say that deep learning is a type of machine learning. It has extra qualities and a special working approach. And selecting any of them to solve a particular problem is depend on the amount of data and complexity of the problem. Machine learning models mostly require data in a structured form. The models are widely appreciated for solving simple as well as complex problems. Also, it uses bits of data to train the system. It helps to find accurate results. So, if someone says what is the difference between machine learning and deep learning then you can explain it. In much simpler terms, it functions just like the human brain. As all the neural networks are connected in the brain, similarly the concept of deep learning works.

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