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Rahul_Srivastava_DL_Assignment.md

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+# Introduction to Deep Learning
+
+Deep learning is a subset of machine learning that is essentially a three-layer neural network.These neural networks aim to imitate the activity of the human brain by allowing it
+to "learn" from enormous amounts of data, albeit they fall far short of its capabilities.While a single-layer neural network may produce approximate predictions, additional hidden
+layers can help to optimise and improve for accuracy.
+
+Many artificial intelligence (AI) apps and services rely on deep learning to improve automation by executing analytical and physical activities without the need for human 
+participation.Everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as upcoming innovations use deep 
+learning technology (such as self-driving cars).
+
+
+## Deep Learning vs. Machine Learning
+
+The first question arises here, if Deep learning is subset of Machine Learning then how it differs ?
+Well answer to this problem , To create predictions, machine learning algorithms use structured, labelled data, which means that certain features are defined from the model's 
+input data and grouped into tables.This isn't to say it never uses unstructured data; it just means that if it does, it usually goes through some pre-processing to convert it 
+to a structured format.
+
+Deep learning eliminates some of the data pre-processing that machine learning generally entails.These algorithms can ingest and interpret unstructured data such as text and photos,
+as well as automate feature extraction, which reduces the need for human specialists. For example, let’s say that we had a set of photos of different pets, and we wanted to categorize
+by "cat", "dog", "hamster", et cetera. 
+
+## How Deep Learning works?
+
+Deep learning neural networks, also known as artificial neural networks, use a combination of data inputs, weights, and bias to try to emulate the human brain.These pieces work together 
+to recognise, classify, and characterise items in the data accurately.Deep neural networks are made up of numerous layers of interconnected nodes, each of which improves and refines the 
+prediction or categorization.This progression of computations through the network is called forward propagation.The visible layers of a deep neural network are the input and output layers. 
+The deep learning model ingests the data for processing in the input layer, and the final prediction or classification is performed in the output layer.
+
+## Deep Learning Applications
+
+`1`. Law Enforcement <br/>
+`2`. Financial Services <br/>
+`3`. Customer Service <br/>
+`4`. Healthcare <br/>
+
+
+Contributor<br/>
+<a href="https://www.linkedin.com/in/rahul-srivastava-882180191/">Rahul Srivastava</a>
+