discuss about neural network and machine learning write 5 lines about each in brief.
Answers
Answer:
Neural networks are a specific set of algorithms that have revolutionized machine learning. ... Neural Networks are themselves general function approximations, which is why they can be applied to almost any machine learning problem about learning a complex mapping from the input to the output space
Explanation:
Approaches to machine learning
Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.
- Supervised learning
Supervised learning typically begins with an established set of data and a certain understanding of how that data is classified. Supervised learning is intended to find patterns in data that can be applied to an analytics process. This data has labeled features that define the meaning of data. For example, you can create a machine-learning application that distinguishes between millions of animals, based onimages and written descriptions.
- Unsupervised learning
Unsupervised learning is used when the problem requires a massive amount of unlabeled data. For example, social media applications, such as Twitter, Instagram and Snapchat, all have large amounts of unlabeled data. Understanding the meaning behind this data requires algorithms that classify the data based on the patterns or clusters it finds. Unsupervised learning conducts an iterative process, analyzing data without human intervention. It is used with email spam-detecting technology. There are far too many variables in legitimate and spam emails for an analyst to tag unsolicited bulk email. Instead, machine-learning classifiers, based on clustering and association, are applied to identify unwanted email.
- Reinforcement learning
Reinforcement learning is a behavioral learning model. The algorithm receives feedback from the data analysis, guiding the user to the best outcome. Reinforcement learning differs from other types of supervised learning, because the system isn’t trained with the sample data set. Rather, the system learns through trial and error. Therefore, a sequence of successful decisions will result in the process being reinforced, because it best solves the problem at hand.
- Deep learning
Deep learning is a specific method of machine learning that incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is especially useful when you’re trying to learn patterns from unstructured data. Deep learning complex neural networks are designed to emulate how the human brain works, so computers can be trained to deal with poorly defined abstractions and problems. The average five-year-old child can easily recognize the difference between his teacher’s face and the face of the crossing guard. In contrast, the computer must do a lot of work to figure out who is who. Neural networks and deep learning are often used in image recognition, speech, and computer vision applications.
- Big data in context of machine learning
Machine learning requires that the right set of data be applied to a learning process. An organization does not have to have big data to use machine-learning techniques; however, big data can help improve the accuracy of machine-learning models. With big data, it is now possible to virtualize data so it can be stored in the most efficient and cost-effective manner, whether on-premises or in the cloud. In addition, improvements in network speed and reliability have removed other physical limitations associated with managing massive amounts of data at the acceptable speed. Add to this the impact of changes in the price and sophistication of computer memory and it’s now possible to imagine how companies can leverage data in ways that would have been inconceivable only five years ago.