Friday, November 29, 2019

Deep Learning



 What Is Deep Learning?

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabeled. Also known as deep neural learning or deep neural network.

How Deep Learning Works

Deep learning has evolved hand-in-hand with the digital era, which has brought about an explosion of data in all forms and from every region of the world. This data, known simply as big data, is drawn from sources like social media, internet search engines, e-commerce platforms, and online cinemas, among others. This enormous amount of data is readily accessible and can be shared through fintech applications like cloud computing.

However, the data, which normally is unstructured, is so vast that it could take decades for humans to comprehend it and extract relevant information. Companies realize the incredible potential that can result from unraveling this wealth of information and are increasingly adapting to AI systems for automated support.

Deep learning learns from vast amounts of unstructured data that could normally take humans decades to understand and process.

Deep Learning Versus Machine Learning

One of the most common AI techniques used for processing big data is machine learning, a self-adaptive algorithm that gets increasingly better analysis and patterns with experience or with newly added data.

If a digital payments company wanted to detect the occurrence or potential for fraud in its system, it could employ machine learning tools for this purpose. The computational algorithm built into a computer model will process all transactions happening on the digital platform, find patterns in the data set and point out any anomaly detected by the pattern.

Deep learning, a subset of machine learning, utilizes a hierarchical level of artificial neural networks to carry out the process of machine learning. The artificial neural networks are built like the human brain, with neuron nodes connected together like a web. While traditional programs build analysis with data in a linear way, the hierarchical function of deep learning systems enables machines to process data with a nonlinear approach.

A traditional approach to detecting fraud or money laundering might rely on the amount of transaction that ensues, while a deep learning nonlinear technique would include time, geographic location, IP address, type of retailer and any other feature that is likely to point to fraudulent activity. The first layer of the neural network processes a raw data input like the amount of the transaction and passes it on to the next layer as output. The second layer processes the previous layer’s information by including additional information like the user's IP address and passes on its result.

The next layer takes the second layer’s information and includes raw data like geographic location and makes the machine’s pattern even better. This continues across all levels of the neuron network.

शस्त्र

सुचत नव्हतं त्याला
करावं काय...
जाहला फार होता
बिचाऱ्यावर अन्याय...
पाटलाकडं गेला,
गावच्या मुखियाकडं गेला, पोलिसांत गेला,
कोणाचाच मिळाला त्याला नाही आधार होता
अन्यायाविरुद्धची लढा लढण्यासाठी
सुचत नव्हतं त्याला
करावं काय..

गडी तो, शोधत होता
रामबाण अन एक जालीम उपाय..
हत्याराची दुकाने असलेल्या
गल्लीत भटकत शोधत
होता एक धारदार शस्त्र...
दुरावले सारे मित्र,
कोणीही काही देईना त्याला
पाहून त्याची फाटलेली वस्त्र...
त्याने मग एक भयाण
जंगल गाठलं,
आपल्या गाठोड्यातून
मग कागद अन लेखणीला
बाहेर काढलं....!

- गणेश