Hello folks today we will be seeing basics about Neural Networks, and its use cases in Machine learning world.
In todays world every little device is using Neural Network to make themselves more more better for the user experience.
Now a days Neural Networks are revolutionizing business and developing everyday life, Neural networks are mathematical models that use learning algorithms inspired by the brain to store information.
Similar to the brain, neural networks are built up of many neurons with many connections between them. Neural networks have been used in many applications to model the unknown relations between various parameters based on large numbers of examples. Examples of successful applications of neural networks are classifications of handwritten digits, speech recognition, and the prediction of stock prices. Moreover, neural networks are more and more used in medical applications.
So lets get started…
What is Neuron?
The biological definition of Neuron is, they (also called neurones or nerve cells) are the fundamental units of the brain and nervous system, the cells responsible for receiving sensory input from the external world, for sending motor commands to our muscles, and for transforming and relaying the electrical signals at every step in between.
But in Machine learning world Neuron is like a Mathematical Function, that is it take some Input and perform mathematics operation on the data and finally give a output.
What is Neural Network?
Neural networks are a set of algorithms, created loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeled or clustering raw input. The patterns they recognize are numerical, contained in vectors, into which all real-world data, be it images, sound, text or time series, must be translated.
Neural networks help us cluster and classify. You can think of them as a clustering and classification layer on top of the data you store and manage. They help to group un-labeled data according to similarities among the example inputs, and they classify data when they have a labeled dataset to train on. (Neural networks can also extract features that are fed to other algorithms for clustering and classification; so you can think of deep neural networks as components of larger machine-learning applications involving algorithms for reinforcement learning, classification and regression.)
What is Feed Forwarding?
Feed Forwarding means Transferring predicted info from one neuron to other neuron or from one hidden layers to an other layer to make the model more accurate.
Neural Network Elements
Deep learning is the name we use for “stacked neural networks”; that is, networks composed of several layers.
The layers are made of nodes. A node is just a place where computation happens, loosely patterned on a neuron in the human brain, which fires when it encounters sufficient stimuli. A node combines input from the data with a set of coefficients, or weights, that either amplify or dampen that input, thereby assigning significance to inputs with regard to the task the algorithm is trying to learn; e.g. which input is most helpful is classifying data without error? These input-weight products are summed and then the sum is passed through a node’s so-called activation function, to determine whether and to what extent that signal should progress further through the network to affect the ultimate outcome, say, an act of classification. If the signals passes through, the neuron has been “activated.
Use cases of Neural Networks are:
Sector Wise:
(a) Marketing
(b) Banking & Finance
(c) Retail & Sales
(d) Medicine (e) Autonomous Industry
Marketing:
Target marketing involves market segmentation, where we divide the market into distinct groups of customers with different consumer behaviour.
Neural networks are well-equipped to carry this out by segmenting customers according to basic characteristics including demographics, economic status, location, purchase patterns, and attitude towards a product. Unsupervised neural networks can be used to automatically group and segment customers based on the similarity of their characteristics, while supervised neural networks can be trained to learn the boundaries between customer segments based on a group of customers.
Medicine:
It is a trending research area in medicine and it is believed that they will receive an extensive application to biomedical systems in the next few years. At the moment, the research is mostly on modelling parts of the human body and recognizing diseases from various scans.
Banking & Finance:
Neural networks have been applied successfully to problems like derivative securities pricing and hedging, futures price forecasting, exchange rate forecasting, and stock performance. Traditionally, statistical techniques have driven the software. These days, however, neural networks are the underlying technologies driving decision making
