Скачать или смотреть Artificial intelligence vs Machine learning vs Deep learning in Tamil with examples

Artificial Intelligence is basically the mechanism to incorporate human intelligence into machines through a set of rules(algorithm). AI is a combination of two words: “Artificial” meaning something made by humans or non-natural things and “Intelligence” meaning the ability to understand or think accordingly. Another definition could be that “AI is basically the study of training your machine(computers) to mimic a human brain and its thinking capabilities”. 

Machine Learning:

 Machine Learning is basically the study/process which provides the system(computer) to learn automatically on its own through experiences it had and improve accordingly without being explicitly programmed. ML is an application or subset of AI. ML focuses on the development of programs so that it can access data to use it for itself. The entire process makes observations on data to identify the possible patterns being formed and make better future decisions as per the examples provided to them. The major aim of ML is to allow the systems to learn by themselves through experience without any kind of human intervention or assistance.

 Deep Learning:

 Deep Learning is basically a sub-part of the broader family of Machine Learning which makes use of Neural Networks(similar to the neurons working in our brain) to mimic human brain-like behavior. DL algorithms focus on information processing patterns mechanism to possibly identify the patterns just like our human brain does and classifies the information accordingly. DL works on larger sets of data when compared to ML and the prediction mechanism is self-administered by machines. 

AI vs. Machine Learning vs. Deep Learning Examples: 

Artificial Intelligence (AI) refers to the development of computer systems that can perform tasks that would normally require human intelligence. 

Some examples of AI include:

There are numerous examples of AI applications across various industries. Here are some common examples:

 Speech recognition: speech recognition systems use deep learning algorithms to recognize and classify images and speech. These systems are used in a variety of applications, such as self-driving cars, security systems, and medical imaging.

Personalized recommendations: E-commerce sites and streaming services like Amazon and Netflix use AI algorithms to analyze users’ browsing and viewing history to recommend products and content that they are likely to be interested in.

Predictive maintenance: AI-powered predictive maintenance systems analyze data from sensors and other sources to predict when equipment is likely to fail, helping to reduce downtime and maintenance costs.

Medical diagnosis: AI-powered medical diagnosis systems analyze medical images and other patient data to help doctors make more accurate diagnoses and treatment plans.

Autonomous vehicles: Self-driving cars and other autonomous vehicles use AI algorithms and sensors to analyze their environment and make decisions about speed, direction, and other factors.

Virtual Personal Assistants (VPA) like Siri or Alexa – these use natural language processing to understand and respond to user requests, such as playing music, setting reminders, and answering questions.

Autonomous vehicles – self-driving cars use AI to analyze sensor data, such as cameras and lidar, to make decisions about navigation, obstacle avoidance, and route planning.

Here are some examples of Machine Learning:

Image recognition: Machine learning algorithms are used in image recognition systems to classify images based on their contents. These systems are used in a variety of applications, such as self-driving cars, security systems, and medical imaging.

Examples of Deep Learning:

Deep Learning is a type of Machine Learning that uses artificial neural networks with multiple layers to learn and make decisions.

 Here are some examples of Deep Learning:

Image and video recognition: Deep learning algorithms are used in image and video recognition systems to classify and analyze visual data. These systems are used in self-driving cars, security systems, and medical imaging.