Artificial Intelligence & Deep learning

We all are interested in listening to songs and seeing videos based on the use of the video sharing social networking platform, YouTube. But, have we ever wondered as to how the app contributes in generating effective recommendations regarding song and movie videos that we would like to hear and see in the near future??

YouTube is armed with Artificial Intelligence or AI to not only generate effective video recommendations based on evaluating your video selection, viewing and sharing but also in deleting objectionable contents.

Did we also ever wonder the manner in which Facebook tags different photos and also generates friend recommendations? The use of AI by Facebook has only made it possible for carrying out different functions like carrying out photo tagging, filtering of fake-news, sorting of timelines and also in providing friend recommendations.

Smart Software Programs or Artificial Intelligence (AI) programs are identified to be computer programs that tend to mimic human behavior based on learning of various types of data patterns and other insights. The AI Programs are combined with that of Machine Learning for helping the users in gaining the advantages of different functionalities and also in enhancing the simplicity of the business processes. The AI Platforms contribute in providing an effective stage for creating an application right from its scratch. Different types of built-in algorithms are incorporated in the AI Platform. The existence of a facility concerning “Drag and Drop” happens to make the AI Platform ready-to-use.

• The list of top AI Technologies that are demanded by the business organizations in the contemporary world are as follows:
• Natural Language Creation
• Speech Recognition
• Virtual Agents
• Machine Learning Platforms
• Decision Management
• Deep Learning Platforms
• Biometrics
• Robotic Process Automation
• Text Analysis and NLP

Machine Learning is identified as a system that has the capability of both acquiring and integrating a significant amount of knowledge from external sources. The Machine Learning Methods are generally categorized into Three Main Types. The same are indicated as follows:

Supervised Learning?: In Supervised Learning, the dataset is already provided and the correct output can be easily foreseen. The problems associated with supervised learning are mainly classified into either regression or classification categories. Regarding regression problems, the results are endeavored to be predicted associated to a continuous output. The input variables are focused on being mapped with some specific function. In classification problems, the results are endeavored to be predicted associated with discrete outputs.

Example

1. One practical example of supervised learning problems is predicting house prices.
2. Who are the unhappy customers?
3. Image classification is a popular problem in the computer vision field. Here, the goal is to predict what class an image belongs to. In this set of problems, we are interested in finding the class label of an image. More precisely: is the image of a car or a plane? A cat or a dog

Unsupervised Learning?:?Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Example

1. Finding customer segments
2. Reducing the complexity of a problem
3. Feature selection

Reinforcement Learning?: Reinforcement Learning is identified as a type of Machine Learning that is developed based on the study of behavioral psychology. The same is concerned with the manner in which software agents tend to take action based on a specific environment so as to enhance the aspect of cumulative rewards?

Deep Learning?is identified to be an effective subset of Machine Learning that encompasses ANNs and Algorithms, which are essentially designed by humans, learning from large datasets. Deep Learning acts like human beings regarding learning wherein a task is carried out based on large numbers of times so that the outcome of the task may tend to improve in a gradual fashion. The Deep Learning Algorithms are observed to survive based on the accumulation of quantifiable amounts of data. In that, the current period reflects the generation of large amounts of datasets, which contribute in the functioning of the computers, the same accounts for the enhancement of strength of the “deep learning algorithms”.