TERM PAPER on
“DEEP LEARNING” Submitted to Amity University Uttar Pradesh
In partial fulfilment of the requirements for the award of the degree of Bachelor of Technology in Computer Science and Engineering by NIKHIL SACHDEVA A2305217637 Under the guidance of
Dr Shipra SaraswatDEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING AMITY SCHOOL OF ENGINEERING AND TECHNOLOGY AMITY UNIVERSITY UTTAR PRADESH
I, Nikhil Sachdeva, student of B.Tech (2-C.S.E.-8(Y)) hereby declare that the project titled “Deep Learning” which is submitted by me to Department of Computer Science and Engineering, Amity School of Engineering Technology, Amity University Uttar Pradesh, Noida, in partial fulfillment of requirement for the award of the degree of Bachelor of Technology in Computer Science and Engineering, has not been previously formed the basis for the award of any degree, diploma or other similar title or recognition.
The Author attests that permission has been obtained for the use of any copyrighted material appearing in the Dissertation / Project report other than brief excerpts requiring only proper acknowledgement in scholarly writing and all such use is acknowledged.
This is to certify that Mr Nikhil Sachdeva, student of B.Tech in Computer Science and Engineering has carried out work presented in the project of the Term paper entitle “Object Detection Using Deep Learning” as a part of First year program of Bachelor of Technology in Computer Science and Engineering from Amity University, Uttar Pradesh, Noida under my supervision.
Dr Shipra SaraswatDepartment of Computer Science and Engineering
The satisfaction that accompanies that the successful completion of any task would be incomplete without the mention of people whose ceaseless cooperation made it possible, whose constant guidance and encouragement crown all efforts with success. I would like to thank Prof (Dr) Name, Head of Department-CSE, and Amity University for giving me the opportunity to undertake this project. I would like to thank my faculty guide Dr Shipra Saraswat who is the biggest driving force behind my successful completion of the project. She has been always there to solve any query of mine and also guided me in the right direction regarding the project. Without her help and inspiration, I would not have been able to complete the project. Also I would like to thank my batch mates who guided me, helped me and gave ideas and motivation at each step.
CONTENTS 1 ABSTRACT
Deep Learning is an artificial intelligence function that shows 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 that has networks capable of learning unsupervised from data that is unstructured or unlabeled.
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 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 a 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.
Deep learning is used across all industries for a number of different tasks. Commercial apps that use image recognition open source platforms with consumer recommendation apps and medical research tools that explore the possibility of reusing drugs for new ailments are a few of the examples of deep learning incorporation.
In the process of investigating Deep Learning previously, you presumably quickly ran over terms like Deep Belief Nets ,Convolution Nets, Back propagation, non-linearity, Image acknowledgment, etc.
Or on the other hand perhaps we ran over the enormous Deep Learning analysts like Andrew Ng, Geoff Hinton, Yann LeCun, Yoshua Bengio, Andrej Karpathy.
The principal thing we have to know is that deep learning is about neural systems.
The structure of a neural system resembles some other sort of system;there is an interconnected web of hubs, which are called neurons, also, the edges that combine them.
A neural system’s primary capacity is to get an arrangement of information sources, perform continuously complex figuring, and afterward utilize the yield to take care of an issue.
Neural systems are utilized for bunches of various applications,
An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurones) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process.
3061970-153670Neural nets are utilized for order undertakings where a question can fallinto one of no less than two distinct classifications .Not at all like different systems like an informal community, a neural system is very organized and comes in layers. The main layer is the input layer, the last layer is the output layer, and all layers in the middle of are alluded to as concealed layers or the hidden layers. A neural net can be seen as the consequence of turning classifiers together in a layered web. This is on account of every hub in the covered up and yield layers has its own particular classifier.
An arrangement of data sources is passed to the principal shrouded layer, the enactments from that layer are passed to the following layer etc, until the point that you achieve the yield layer, where the consequences of the grouping are dictated by the scores at every node. This occurs for each arrangement of information sources.
This arrangement of occasions beginning from the info where every enactment is sent to the following layer, and after that the following, the distance to the yield, is known as forward engendering, or forward propagation. Forward prop is a neural net’s method for ordering an arrangement of sources of info.