Home Articles How Machine Language Differs from Neural Networks

How Machine Language Differs from Neural Networks

79
0

Machine Language and Neural Networks are probably one of the hottest tech topics right now. Read on to know more about how they differ…

Machine Language (ML) and Neural Networks are probably one of the hottest tech topics right now. Large enterprises and young startups alike are all gold-rushing this exciting field. If you think that machine language and neural networks are similar, then you should care about knowing their difference.

Machine Language
Machine Learning is an application or the subfield of Artificial Intelligence (AI). Machine Learning enables a system to automatically learn and progress from experience without being explicitly programmed. Machine Learning is a continuously developing practice. The goal of Machine learning is to understand the structure of data and fit that data into models, these models can be understood and used by people. In Machine Learning generally, the tasks are classified into broad categories. These categories explain how learning is received, two of the most widely used machine learning methods are supervised learning and unsupervised learning.

In essence, machine learning is set of algorithms that parse data and learns from the parsed data and use those learnings to discover patterns of interest. Some of skills required for Machine Learning are Probability and Statistics, Programming Skills, Data Structures and Algorithms, Knowledge about machine learning frameworks, Big Data and Hadoop. Some of the verticals in which Machine Learning is used are Health Care, Retail, E-Commerce, Online recommendations, Tracking price changes, Better Customer Service and Delivery Systems. Siri, Google Maps and Google Search etc. uses Machine Language.

We need machine learning for tasks that are too complex for humans to code directly, i.e. tasks that are so complex that it is impractical, if not impossible, for us to work out all of the nuances and code for them explicitly. So instead, we provide a machine learning algorithm with a large amount of data and let it explore and search for a model that will work out what the programmers have set out to achieve.

Neural Networks
On the other hand, Neural Network or Artificial Neural Network is inspired by the structure of the brain. The neural network contains highly interconnected entities, called units or nodes. Neural networks are deep learning technologies. It generally focuses on solving complex processes. A typical neural network is a group of algorithms, these algorithms model the data using neurons for machine learning.

In essence, neural network is one set of algorithms used in machine learning for modelling the data graphs of Neurons. Some of skills required for Neural Networks are Probability and Statistics, Data Modelling, Programming Skills, Data Structures and Algorithms, Mathematics, Linear algebra and graph theory. Some of the verticals in which Neural Networks is used are Finance, Health Care, Artificial Intelligence based Applications, Stock Exchange Prediction. Image Recognition, Image Compression, and Search Engines etc. uses Neural Networks.

A Brief Conclusion
Machine Learning and Neural Networks falls under the same field of Artificial Intelligence, wherein Neural Network is a subfield of Machine Learning, Machine learning serves mostly from what it has learned, wherein neural networks are deep learning that powers the most human-like intelligence artificially. We can conclude it by saying that neural networks or deep learnings are the next evolution of machine learning. It explains how a machine can make their own decision accurately without any need for the programmer telling them so.

LEAVE A REPLY

Please enter your comment!
Please enter your name here

− 3 = 4