Artificial Intelligence, Machine Learning And Deep Learning

As,

 research in the field of AI is also growing, so the previous benchmarks that define Artificial intelligence (AI) are now becoming obsolete.

                   From this development came new terms such as machine learning and deep learning. But sometimes, there are slices between AI, machine learning, and deep learning, so the difference between them can be very unclear. So in this post, I’ll give a quick explanation of what AI, machine learning, and deep learning mean and how they are different.

Artificial intelligence, machine learning, and deep learning

When viewed from the picture we get a little picture that machine learning is part of AI and deep learning is part of machine learning.

Artificial Intelligence

•               AI has been studied for decades and is still one of the most difficult subjects to understand in Computer Science. This is partly because of how large and vague the subject is. This has Applications in almost every way we use computers in society. AI is anything that refers to the simulation of human intelligence, machines that are programmed to think like humans and imitate their actions.
• AI typically analyzes its environment and takes actions that maximize its chances of success. In early days, approaches to AI are such as formal logic and expert systems. These methods dominated AI at the time.,
• However as the development of computational power, greater emphasis on solving specific problems, and also there are new ties between AI and other fields. And one method that rises by the effect of this is learning or we will called it machine learning.
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Machine Learning

       

  

• In accordance with the words, machine learning means machines that learn from data. 
• Machine learning is closely related to computational statistics, which focus on making predictions. Data mining is also related to this study,which focuses on exploratory data analysis through unsupervised learning.
• In machine learning, there are several types of algorithms used and are grouped based on the expected input and output of the algorithm.

Supervised Learning

• Supervised learning create functions that map an input to the desired output, for example in classification. It observed patterns of data and converts them into model to predict future data. 
• An example of a method that is included in supervised learning is neural network, kNN, decision trees, naïve bayes, SVM, etc.

  

Unsupervised Learning.

Unsupervised learning models the input set, such as clustering. Unlike the classification that each data has a class. Clustering works by grouping similar data.

             An example of a method that is included in supervised learning is k-means, DBSCAN, etc.

Reinforcement Learning

• Reinforcement learning is a learning algorithm that is applied to intelligent agentsso that they can adjust to the conditions in their environment, this is achieved by maximizing the value of the ‘reward’ prize that can be achieved. This type teaches how to act to deal with a problem, an action that has an impact.

Deep Learning

Deep learning is machine learning algorithms based on learning multiple levels (i.e deep) of representation/abstraction inspired by the structure and function of the brain called artificial neural networks. Basically deep learning is a large neural network

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• There are many methods in deep learning, such as Convolutional Neural Network (CNN), Long Short Term Memory (LSTM), etc. 

• The following are examples of the application of deep learning.

  • Self-driving car.
  • Machine translation.
  • Image colorization.

Thanks for reading !!!

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