Concepts and Applications of Deep Learning

Concepts and Applications of Deep Learning Abstract: Since 2006, Deep Learning, also known as Hierarchal Leaning has been evolved as a new field of Machine Learning Research. The deep learning model deals with problems on which shallow architectures (e.g. Regression) are affected by the curse of dimensionality. As part of a two-stage learning scheme involving multiple layers of nonlinear processing a set of statistically robust features is automatically extracted from the data. The present tutorial introducing the deep learning special session details the state-of-the-art models and summarizes the current understanding of this learning approach which is a reference for many difficult classification tasks. Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and tex t. Introduction: Just consider we have to identify someone’s handwriting. The people have different ways of writing, for example, the numbers-Whether they write a ‘7’ or a ‘9’. We know that if there is a close loop on the top of the vertical line then we named it as ‘9’ and if it contains a horizontal line instead of loop then we think it is ‘7’. The thing we used for exact recognition of digit is a smart display of setting smaller features together to make the whole detecting distinguished edges to make lines, observing a horizontal vs. vertical line, seeing the positioning of the vertical section under the horizontal section, detecting a loop in the horizontal section, etc. The idea of the deep learning is the same: find out multiple levels of features that work jointly to define increasingly more abstract aspects of the data. So, Deep Learning is defined as follows: â€Å"A sub-field of machine learning that is based on learning several levels of representations, corresponding to a hierarchy of features or factors or concepts, where higher-level concepts are defined from lower-level ones, and the same lower-level concepts can help to define many higher-level concepts. Deep learning is part of a broader family of machine learning methods based on learning representations. An observation (e.g., an image) can be represented in many ways (e.g., a vector of pixels), but some representations make it easier to learn tasks of interest (e.g., is this the image of a human face?) from examples, and research in this area attempts to define what makes better representations and how to learn them.† see Wikipedia on â€Å"Deep Learning† as of this writing in February 2013; see http://en.wikipedia.org/wiki/Deep_learning. The performance of recent machine learning algorithms relies greatly on the particular features of the input data. As for example marking emails as spam or not spam, can be performed by breaking down the input document intowords. Selecting the exact feature representation of input data, or feature engineering, is a technique that people can recall previous knowledge of an area to enhance an algorithms computational performance and accuracy. Moving towards general artificial intelligence, algorithms need to be less dependent on this feature engineering and better learn to classify the descriptive factors of input data on their own. Deep learning approaches is useful among many domains: it has had great commercial success powering most of Google and Microsofts current speech recognition, digital image processing, natural language processing, object recognition, etc. Facebook is also planning on using deep learning approaches to understand its users. How to build a deep representation of input data? The main idea is to learn a hierarchy of features one level at a time where the input to one computational level is the output of the previous level for an arbitrary number of levels. Otherwise, shallow representations (most current algorithms like regression) go directly from input data to output classification. Inspirations for Deep Architectures The main inspirations for studying learning algorithms for deep architectures are the following: The brain has a deep architecture The visual cortex is considered and demonstrates an order of regions all of them have a representation of the input, and signals move from one to the next. In case there are also miss connections and at some level parallel paths, so the picture is more complicated). Each level of this feature hierarchy represents the input at a different level of concept, with more abstract features further up in the hierarchy, defined in terms of the lower-level ones. Note that representations in the brain are in between dense distributed and purely local: they arelight: about 1% of neurons are active concurrently in the brain. Given the vast number of neurons, this is still a very efficient (exponentially efficient) representation. Cognitive processes seem deep Humans organize their ideas and concepts hierarchically. Humans first learn simpler concepts and then compose them to represent more abstract ones. Engineers break-up solutions into multiple levels of abstraction and processing. Introspection of linguistically expressible concepts also suggests alightrepresentation: only a small fraction of all possible words/concepts are applicable to a particular input (say a visual scene). One good analogue for deep representations is neurons in the brain (a motivation for ANN) the output of a group of neurons is given as the input to more neurons to form a hierarchical layer structure. Each layerNis composed ofh computational nodes that connect to each computational node in layerN+1. See the image below for an example: Related Work: Historically, the concept of deep learning was originated from artificial neural network research. (Hence, one may occasionally hear the discussion of â€Å"new-generation neural networks†.) Feed-forward neural networks or MLPs with many hidden layers, which are often referred to as deep neural networks (DNNs), are good examples of the models with a deep architecture. Back-propagation (BP), popularized in 1980’s, has been a well-known algorithm for learning the parameters of these networks. Unfortunately back-propagation alone did not work well in practice then for learning networks with more than a small number of hidden layers (see a review and analysis in (Bengio, 2009; Glorot and Bengio, 2010). The pervasive presence of local optima in the non-convex objective function of the deep networks is the main source of difficulties in the learning. Back-propagation is based on local gradient descent, and starts usually at some random initial points. It often gets trapped in poor local optima when the batch-mode BP algorithm is used, and the severity increases significantly as the depth of the networks increases. This difficulty is partially responsible for steering away most of the machine learning and signal processing research from neural networks to shallow models that have convex loss functions (e.g., SVMs, CRFs, and MaxEnt models), for which global optimum can be efficiently obtained at the cost of less modeling power. The applicative domains for deep learning: In natural language processing, a very interesting approach gives a proof that deep architectures can perform multi-task learning, giving state-of-the-art results on difficult tasks like semantic role labeling. Deep architectures can also be applied to regression with Gaussian processes [37] and time series prediction. Another interesting application area is highly nonlinear data compression. To reduce the dimensionality of an input instance, it is sufficient for a deep architecture that the number of units in its last layer is smaller than its input dimensionality. Moreover, adding layers to a neural network can lead to learning more abstract features, from which input instances can be coded with high accuracy in a more compact form. Reducing the dimensionality of data has been presented as one of the first application of deep learning. This approach is very efficient to perform semantic hashing on text documents, where the codes generated by the deepest layer are used to build a hash table from a set of documents. A similar approach for a large scale image database is presented in this special session. Conclusion: Deep learning is about creating an abstract hierarchical representation of the input data to create useful features for traditional machine learning algorithms. Each layer in the hierarchy learns a more abstract and complex feature of the data, such as edges to eyes to faces. This representation gets its power of abstraction by stacking nonlinear functions, where the output of one layer becomes the input to the next. The two main schools of thought for analyzing deep architectures areprobabilisticvs.direct encoding. The probabilistic interpretation means that each layer defines a distribution of hidden units given the observed input,P(h|x). The direct encoding interpretation learns two separate functions theencoderanddecoder- to transform the observed input to the feature space and then back to the observed space. These architectures have had great commercial success so far, powering many natural language processing and image recognition tasks at companies like Google and Microsoft.

Rabies: Closer Than You Think :: science

Rabies: Closer Than You Think Rabies, a virus of the nervous system and salivary glands is a fast moving killer; it’s not something to mess around with. Rabies comes from the Latin word â€Å"to rage†. Rabies is easily associated with rage. When people think of rabies, they usually think of a mad raccoon or dog, foaming at the mouth and running around crazy; dying soon after. The thought of going crazy is a pretty reasonable guess for how rabies torments its victims. The virus enters through a bite or transfer of infected saliva and makes its way through the nerves toward your spinal cord and brain. Obviously, rabies is an extremely deadly virus that affects the nervous system. Immediately after being bitten, you need to seek medical attention or death will come within a week. Rabies is a very fatal virus that, without proper medical attention, will kill its victims very swiftly, but there are ways to help. There is a vaccine for people who are likely to get rabies, and there is a vaccine that, if used immediately after the exposure to the rabid animal, can save the victim of rabies. These vaccines have saved the lives of many. Medical technology at its finest is what saves victims of these horrible diseases, but if you are too late and do not receive the proper treatment in time, well, death is a lot closer than you think. Rabies is a disease that requires fast treatment. Go too slow and all you can do is wait until death comes; painfully and tormenting you until you draw your last breath. Most often the cause of contamination is through the bite of a rabid animal. The virus then spreads through the nerves until it reaches the central nervous system (CNS) which is the spinal cord and the brain. Then the virus incubates in the infected creature’s body for approximately 3-12 weeks. The victim shows no signs of illness during this â€Å"incubation period†. When the virus reaches the brain, it multiplies rapidly, passes to the salivary glands, and the infected creature begins to show signs of disease. The infected creature usually dies within 1 week of becoming sick. Within four or five days, the victim my then either slip into a months long coma ending in death or die suddenly of cardiac arrest. Rabies is extremely dangerous. It’s important to treat the wound when you have been bitten, but the disease isn’t always transmitted through a bite.

Research Proposal on Banking Essay

Introduction Over thirty-five years have passed since academics began speculating on the impact that information technology (IT) would have on organizational structure. The debate is still on-going, and both researchers and managers continue to explore the relationship between IT and organizational structure. As organizations need to process more information under these uncertain conditions, IT is one possible way for organizations to increase their information processing capability. We are conducting a research in HBL bank that, how bank increase the number of account holders using Internet banking among its Current Account holders. IT has a dramatic effect on both people’s personal and professional lives. IT is also changing the nature of organizations by providing opportunities to make fundamental changes in the way they do business. The technology is changing rapidly, with computing speeds and the numbers of transistor equivalents available in a given area of a microprocessor chip both doubling in very short time. Organizations are acquiring more and more technology systems to assist in everything from manufacturing to the management of information to the provision and improvement of customer service. Harnessing and coordinating this computing power is the challenge. New tools and innovative perspectives with which to examine, interpret, and comprehend these rapidly evolving environments are always needed and sought. Background / Literature View: IT is transforming the way that business is conducted. Computers prepare invoices, issue checks, keep track of the movement of stock, and store personnel and payroll records. Word processing and personal computers are changing the patterns of office work, and the spread of information technology is affecting the efficiency and competitiveness of business, the structure of the work force, and the overall growth of economic output. Many people believe that the primary driving force behind this information revolution is progress in microelectronic technology, particularly in the development of integrated circuits or chips. Thus, the reason that computing power that used to fill a room and cost $1 million now stands on a desk and costs $500 or that pocket calculators that used to cost $1000 now cost $10 is that society happens to have benefited from a series of spectacularly successful inventions in the field of electronics. But fewer people understand why the introduction of information technology occurred when it did or took the path that it did, why data processing came before word processing or why computers transformed the office environment before they transformed the factory environment. Because this technology oriented view of the causes of the information revolution offers little guidance to the direction that technological developments have taken thus far, it offers little insight into the direction that they will take in the future. Electronic banking is one of the first things that come to mind when one thinks about the future of banking. It is generally assumed that electronic banking is new and that it will replace or supplement many channels of delivery of retail banking services. The term electronic banking as used here refers to any banking activity accessed by electronic means. It includes Online Banking, Automated Teller Machines (ATMs), Automated Call Centers, Digital Cash, Internet Banking, Screen Telephones, E-Utility Bills and so on. These channels of delivery can be used for presenting and paying bills, buying and selling securities, transferring funds, and providing other financial products and services. Electronic banking can be used for retail banking and business-to-business (B2B) transactions, as well as for facilitating large-amount transfers. Equally important, electronic banking is a worldwide phenomenon. As the term is used here, it involves transactions. Web sites that are transactional are considered electronic banking. Electronic banking and the Internet in general are forcing a shift in the way banks and other businesses organize and the way they think of themselves. A shift is taking place from vertical integration to virtual integration. Banks and other financial intermediaries must realize that they are in the financial information industry. The Internet makes it possible to bring both customers and suppliers together to share critical business information. E-banking helps banks relay and show to their clients how good their services are, how many services we are providing and that the services they offer are of better standards. Through E-banking the company can show the clients that they are better than competitors and can give them satisfaction guaranteed. Statement of the problem The internet and the different things it can do to uplift business procedures, products and services is a current necessity for business. One of internet’s products is Electronic banking. Electronic banking is a faster way for clients to transact with the banks personnel. Clients can still transact with banks while on the comfort and safety of their homes and Offices. The main purpose of this proposed research is to determine, how we will increase Internet Banking Users among their present Account holders. Theoretical Framework: Increase Internet Banking Users Quality Products (websites) A Bank Manager observed that, if he provides the better Quality Products (Websites) and Low Bank Charges on Internet-Banking to his Account holders. This will increase the number of Customer of Internet Banking. But it will not affect on those Account holders with less qualification and do not use internet. Qualification of account holder and use of internet Low Bank Charges on Internet Banking Research Objectives: This Research intends to find out, If Bank updates its website and gives quality products, easy use and as well as reduce its transaction charges on Internet banking will increase the number of users using Internet Banking among its Account holders. However, according to the literature review, the qualification of account holder is plays an important part in this relationship. Our objective is if we increase quality products and reduce its transaction charges on Internet banking will increase the Internet Banking users. Research Question: * If we increase quality products and reduce its transaction charges on Internet banking will increase the Internet Banking users or not? * Qualification of customer and use of internet is effect on internet banking users or not? Research Design/Methodology: Type of research This research will use the descriptive type of research. Descriptive method of research is to gather information about the present existing condition and solving the problem. The descriptive approach is quick and more practical financially. Moreover, this method will allow for a flexible approach, thus, when important new issues and questions arise during the duration of the study, a further investigation may be allowed. The study opted to use this kind of research considering the goal of the study to obtain first hand data so as to formulate rational and sound conclusions and recommendations for the study. Research Strategy For this research data will be gathered through collating published studies from different books, articles from different related journals and studies, and other literary instruments. Afterwards make a content analysis of the collected documentary and verbal material. The study will then summarize all the necessary information. The study will then make a conclusion based on the said information and provide insightful recommendations on how to solve the said problem. Sample and Sampling Technique The respondents of the research came from the different branches of bank mentioned from Karachi. Due to time constraint and also, for the convenience of the researcher, only hundred (100) respondents were considered for the study. The convenience sampling technique was imposed in the study to pick up the hundred respondents, mainly because the availability of the respondents from the different branches was considered. This part of the study is important because the most important data needed to fulfill the objectives and aims of this study will only be supplied by the respondents from the Branches of HBL bank Karachi. Primary and secondary data collection The primary source of data will come from a survey using questionnaire and interviews that will be conducted by the researcher. The primary data frequently gives the detailed definitions of terms and statistical units used in the survey. These are usually broken down into finer classifications. The secondary source of data will come from research through the internet; books, journals, related studies and other sources of information. Acquiring secondary data are more convenient to use because they are already condensed and organized. Moreover, analysis and interpretation are done more easily. Validation of the instrument For validation purposes, the researcher pre-tested a sample of the set survey questionnaires. This was done by conducting an initial survey to at least five respondents from the different banks from Karachi. After the respondents answered, the researchers then asked them to cite the parts of the questionnaire that needs improvement. The researcher even asked for suggestions and corrections from the respondents to ensure that the survey-questionnaire is effective. Automatically, these five respondents were not included as respondents for the study. Data analysis Data gathered will be analyzed through frequency distributions. These will give way to reviewing the data categories and the number of referrals in each category. The data acquired will be analyzed according to the different categories and importance. The information that will be gathered and analyzed will be important to achieve the objectives desired by the study.

Why Natural Law Theory Is an Inadequate Criticism of...

Albert Einstein one said, Common sense is the collection of prejudices acquired by age eighteen.(Quotations,162) There is some truth to what he said in relation to Natural Law Theory. It would seem that Natural Law is based at least in part on common sense. This essay will attempt to discredit the Theory of Natural Law on these grounds, as well as proving that it is inapplicable when judging the ethical value of homosexuality, and discrediting homosexuality as a perversion. Act utilitarianism depicts the argument more clearly, because there are certain semantic inconsistencies with Kantian ethical Theory that will be discussed further on. Let us first consider the premise that homosexuality is contrary to Natural Law, because the†¦show more content†¦Viagra) or building alliances are against Natural Law Theory. We must also question whether irrational species are ever capable of doing that which is unnatural, as they are guided by instinct rather than coherent thought. If we expand this into the human sphere, it is then clear that only humans are capable of unnatural activity. Given our ability to choose and our similarity to these species, humanity can only have one of the following two potential characteristics: humans are either naturally inclined to homosexuality, or they are unnatural. Because humans are rational (and because of which, perhaps unnatural) and can therefore choose among alternatives, we may say that humanity is naturally inclined towards homosexuality. However, ethics is concerned with right and wrong, not with what is. Act utilitarianism can clearly explain homosexuality as a general practice. Surely heterosexuals enjoy being heterosexuals. There is no reason for a woman to say, I love men, but I wish I loved women, for that would mean that she is either considering women in the light of an intimate relationship and/or sex (and is perhaps considering bisexuality), or she is recalling characteristics that have nothing to do with either (i.e. women are more organized) and will hence have nothing to do with her sexual orientation. It is likewise for homosexuals, with one exception. TheShow MoreRelatedCrime, Deviance, Social Order And Social Control3729 Words   |  15 PagesDefinitions of: Crime, Deviance, Social order and Social control Crime is defined by the Oxford dictionary as ‘an action or omission which constitutes an offence and is punishable by law.’ (Dictionary, 2015) Whereas deviance is failing to conform to the expectations held by society without necessarily breaking any laws. 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