What is neuron and artificial neuron in deep learning?

Deep learning is a form of machine learning that uses a model of computing that’s very much inspired by the structure of the brain.

With the help of deep learning, we are trying to mimic the human brain. We are trying to train the machine the way human brain thinks and works.

To get better understanding of how does machine learning works, we need to first understand how human brain works.

The brain works with the help of brain cells which we called as neurons. Neurons has dendrites which receive signals from other neurons. The cell body which is surrounded by the dendrites receive all the signals and summed them and forward these signals to axons. With the help of axons these signals are fired to the next neurons. The next neuron is present at some distance and that distance is known as synapse. It fires only to the next neuron when the signals coming from cell body exceeds a particular limit.

By copying the same process we can create artificial neuron and it does the same function as the brain does. Read more about Machine Learning Algorithms: When To Use Which Machine Learning Algorithm

Now the questions comes, if our brain can does all the work then we need artificial neuron. The advantage of having artificial neuron is that it can mimic our brain by getting training data. It never ever tires, it can handle multiple inputs without any causes and provide more likely accurate answers with lot of permutations and combinations which human brain can never does.
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All About Machine Learning Algorithms: When To Use Which Machine Learning Algorithm

People always find hard to understand which machine learning algorithm has to be applied on given problem. Today we have infinite number of problem and we might think that we have infinite number of solution available around it. But when we start classifying the problems in different categories we usually find that these infinite problems can fir in finite categories and infinite solutions change to finite solutions. In this post, will try to explain more on what is an Algorithm and under which circumstances we have to use which type of machine learning algorithm.

To tell a computer what it has to do, in that case you need to write a program. A program is nothing but a set of instructions in the form of some syntax. This syntax can be written in any programming language like Java, C, Python, Ruby on Rails etc. e.g if you have to write a program to print 1 to 20 numbers, in this case you can opt for any kind of programming language. The syntax will be different but the logic will remain be the same. Now the question comes what is Logic? Logic is nothing but it’s an algorithm. An algorithm is a step by stem procedure of solving a problem in computer world.

Machine learning focuses on the development of the computer programs that can change when exposed to new data. Learning comes from the past experience and accordingly Machine Learning tune to itself. Read More about types of Machine Learning

How Does Machine Learning Helps To Solve Any Kind Of Problem? The problem can be divided into different kind of category as mentioned below:- 1. Classification Problem: When we know that any problem is having set number of outputs during that time we can use classification algorithm. E.g. Differentiating between apple and oranges is a type of classification problem.
2. Anomaly Detection Problem: When a same type of input is given to system and if any deviations happen in that input in that case anomaly detection algorithm is used to detect that change. It analyzes a certain type of pattern and alerts whenever there is change in pattern.
3. Regression Problem: Whenever we want machine to give us a number as output during that time we have to apply the regression algorithm. It is used to calculate numeric values. E.g. what is the minimum investment should I put in to become millionaire in 10 years.
4. Clustering Problem: Trying to find what is the structure behind the given data sets during that time clustering algorithm is required. By understanding how data is organized you can better predict the behavior of particular event.
5. Reinforcement Problem: When decision has to be made in that case, reinforcement learning algorithm is applied.

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Difference Between Artificial Intelligence, Machine Learning and Deep Learning

Artificial Intelligence is the capability of a machine to imitate intelligent human behavior. It is accomplished by studying how human brain things, learns, decide and work while trying to solve a problem. The main applications of Artificial Intelligence are Speech Recognition, Image Recognition, Self-Driving Cars, Self-Driving Networks, Siri, YouTube and Pandora. AI was first coined in 1956 but due to limitation of computation network it couldn’t be used that time.

After AI, Around 1990’s Machine Learning came into picture. Machine Learning in nothing but is type of Artificial Intelligence that provides computers with the ability to learn without being explicitly programmed. Machine Learning is of different types and can be found in the previous post.
Machine learning couldn’t fly high because of its below mentioned limitations:
1. Data with large number of inputs and outputs
2. High Dimensionality of data
3. It can solve NLP and Image Recognition up to some extent but not at deep level.
4. It doesn’t support feature extraction. Feature extraction is nothing but it’s a way to solve the problem without giving all the required inputs.

Click here To know more about Machine Learning Algorithms: When To Use Which Machine Learning Algorithm Deep Learning is subset of Machine Learning. It came into existence around 2005-2006 and the motive behind Deep learning is to overcome the existing problems of Machine Learning. Deep Learning is collection of statistical machine learning techniques used to learn feature hierarchies often based on artificial neural networks. Deep Learning models are capable to focus on the right features by themselves but requiring some little guidance from the programmer. These models also solve the dimensionality problem too. The main idea behind Deep Learning is to build learning algorithms that mimic brain. It is implemented with the help of neural network.

Click here to know about what is neuron in deep learning.

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