Neural Networks and Their Applications 

Student Name

Professor Rodriguez

ENG 1103 – First Year Composition

17 November 2020

Bibliographic Outline

Neural Networks and Their Applications  <- This is the student’s working title

A common joke at the expense of computer scientists is that trees—the data structure—are drawn with the root node at the top and the leaf nodes at the bottom because the computer scientists who formed that convention never bothered to go outside and look at a real tree. If that is true, then modern computer scientists have truly surpassed their predecessors by designing their algorithms based on the most complicated biological structure currently known: the human brain. Through this study, they created the artificial neural network, an incredibly powerful, deep, and versatile machine learning algorithm that essentially functions as a connected, layered set of simulated neurons. As the algorithm grew in popularity, many variations of it began to branch off into their own, more specialized algorithms, which all perform better in specific situations. However, this is not always readily apparent, and the terminology can be very confusing. Some of the more common types of neural networks are convolutional neural networks, recurrent neural networks, and generative adversarial networks, and their individual uses will be discussed subsequently.

 

 

Section 1: Background Information <-this section title is just for the bib outline. On the final paper, it will be removed

1. To understand specific types of neural networks, though, it is important to understand neural networks generally. <- Topic sentence
2. Sources: <-Notice how he gives the complete citation as it will appear in his paper
   1. Pouyanfar, Samira, et al. “A Survey on Deep Learning.” ACM Computing Surveys (CSUR), vol. 51, no. 5, 2019, pp. 1–36. https://na03.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=37839874190002901&institutionId=2901&customerId=2900. Accessed 8 Oct. 2020.
   2. Schmidhuber, Jürgen. “Deep Learning in Neural Networks: An Overview.” Neural Networks, vol. 61, 2015, pp. 85–117. https://na03.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=37840903500002901&institutionId=2901&customerId=2900. Accessed 14 Oct. 2020.
3. Sentences:
   1. Deep learning algorithms are defined by having multi-layered structures where each layer extracts key features from the input data at progressively higher levels.
   2. Each neuron computes the weighted sum of the values of the previous layer’s neurons, then feeds that sum to an activation function whose purpose is to map the output to a desired range.

Section 2: Convolutional Neural Networks

1. One type of neural network is the convolutional neural network, which uses special filters called kernels to extract complex features from the given data.
2. Sources:
   1. Teuwen, Jonas, and Nikita Moriakov. “Chapter 20 – Convolutional Neural Networks.” Handbook of Medical Image Computing and Computer Assisted Intervention, edited by Kevin Zhou et al., Academic Press, 2020, pp. 481–501. ScienceDirect. www.sciencedirect.com/science/article/pii/B9780128161760000259. Accessed 29 Oct. 2020.
   2. Maggiori, Emmanuel, et al. “Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification.” IEEE Transactions on Geoscience and Remote Sensing, vol. 55, no. 2, 2017, pp. 645–657.  https://na03.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=37839428510002901&institutionId=2901&customerId=2900. Accessed 8 Oct. 2020.
3. Sentences:
   1. Because kernels are generally localized, CNNs work very well on tasks where pieces of data that are close to each other are more likely to be related, such as images.
   2. A CNN can be thought of as a neural network that takes convolved inputs instead of raw inputs, and it learns in much the same way as a traditional neural network otherwise.

Section 3: Recurrent Neural Networks

1. Another type of neural network, the recurrent neural network, tends to be most useful in situations where looking at a single training example is ineffective and context is required.
2. Sources:
   1. DiPietro, Robert, and Gregory D. Hager. “Chapter 21 – Deep Learning: RNNs and LSTM.” Handbook of Medical Image Computing and Computer Assisted Intervention, edited by Kevin Zhou et al., Academic Press, 2020, pp. 481–501. ScienceDirect. https://www-sciencedirect-com.proxy.library.cpp.edu/science/article/pii/B9780128161760000260. Accessed 29 Oct. 2020.
   2. Pouyanfar, Samira, et al. “A Survey on Deep Learning.” ACM Computing Surveys (CSUR), vol. 51, no. 5, 2019, pp. 1–36. https://na03.alma.exlibrisgroup.com/view/action/uresolver.do?operation=resolveService&package_service_id=37839874190002901&institutionId=2901&customerId=2900. Accessed 8 Oct. 2020.
3. Sentences:
   1. A recurrent neural network is one that essentially maintains a short-term memory of previous inputs and uses its memorized inputs to determine its output.
   2. RNNs are very useful in areas such as natural language processing where context is absolutely necessary; to understand the meaning of a single word in the sentence, the neural net would need to look at the words before it as well.

Section 4: Generative Adversarial Networks

1. The final type of neural network, the generative adversarial network (GAN) is different from the other two in that its primary goal is to synthesize data that is very realistic instead of performing an operation on existing data.
2. Sources:
   1. Hong, Yongjun, et al. “How Generative Adversarial Networks and Their Variants Work.” ACM Computing Surveys (CSUR), vol. 52, no. 1, 2019, pp. 1–43. https://dl-acm-org.proxy.library.cpp.edu/doi/abs/10.1145/3301282. Accessed 29 Oct. 2020.
3. Sentences:
   1. A GAN works by having two neural networks: one to generate images, and one to try to choose which images are generated and which are real, and they both compete against each other, training themselves in the process.
   2. GANs are commonly used to generate realistic-looking images, and well-trained GANs can produce images that are indistinguishable from real captured images without looking very closely.

 

 

 

Discussion

1. Neural networks are highly varied, each with their own specific method of functioning and their own optimal use case. <-Because this is an explanatory essay, the student is combining the topic sentence and the direction of the discussion into one sentence!
2. CNNs tend to be useful on things such as images, where data tends to be close to what it is related to over multiple decisions. Convolutions also prove very useful when dealing with large training examples, because they can reduce the size of the data before it gets to the neural network proper. RNNs, meanwhile, are useful in situations where it is necessary to look at previous examples when making a conclusion, which lends them well to working with sequences of data points such as words or musical notes. GANs are separate, and should be used for projects involving image generation. <- Since this is an explanatory research paper on the different types of networks, the discussion section is mostly concluding when and how they can be used. This student overrode requirement C. by simply writing more sentences. You can do it this way, or you can write a bullet about your discussion point and then two sentences that would be in this section.

Conclusion

1. This is incredibly important to know when doing projects involving neural networks because using a given type of network on unsuited data would drastically decrease the efficiency of the algorithm, making it very difficult to perform the task. <-for this section, the student did not look to the future and was a bit confused. Do not follow this section as a guide.
2. However, this information should make any kind of neural network-related project much easier by providing context on what types should be used in which situations and how best to use them.

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