2500 word essay over deep learning in APA format. Attached are the files needed to complete the essay. Essay will be run through turnitin so absolutely no plagiarism. Essay will consist of Abstract, Introduction, Literature Review, Conclusion, and References. At least 10 references, 3 already provided. Must conclude upon research question

Outline and Abstract due by Feb. 19th at 1:00 PM

Final Outline due by Feb. 25th at 3:00 PM

Final Paper due by Mar. 3rd at 5:00 PM

Running Head: Deep Learning 1 Deep Learning
By:
Nathaniel Powell Running Head: Deep Learning 2 Concept Statement:
With the advancement of technology and the continuous growing of various applications
to become more time efficient and trying to obtain accuracy and consistency. There will need to
be a way in which all this information and data will need to be combined to become user
friendly. This is happening with deep learning.
Deep learning is defined as a branch of machine learning based on a set of algorithms that
attempt to model high-level abstractions in data. Deep Learning is comprised of various
applications that may be applied including image recognition, object recognition, speech
recognition, and traditional classification methods. The combinations of these applications will
simplify the tasks that must be performed by computers in order to put out the solution or answer
being searched for by the user. Originally we would write programs for computers to follow, but
what if there is an error in the information that entered? How will the computer then know what
to do?
In order for these tasks to be completed algorithms must be developed that will enable a
computer to perform the necessary steps in solving the answer. These algorithms will allow our
computers to oversee thousands of examples with the correct solutions and from them generate
new solutions. Because of deep learning application has multiple advantages which a big issue
would be reducing the need for featuring engineering which is very time consuming. It will also
be more time efficient and create faster output. But there also comes disadvantages to deep
learning. Deep learning require large amounts of data and when being trained it is not the easiest
application to comprehend and will take a longer time because of the complexity.
So with deep learning being equipped and user properly business will be able to benefit
from this. This program will be very useful because it was inspired by our brain function to teach
itself when mass amounts of information are taken in. So the program will be able to take in
large amounts of data and be able to classify and organize it to the proper “location”.
References: Running Head: Deep Learning
Blog, G. (Ed.). (2015, September 5). Building a Deeper Understanding of Images. Retrieved
January 29, 2017, from https://research.googleblog.com/2014/09/building-deeperunderstanding-of-images.html
Dahl, G. E. (n.d.). Deep learning approaches to problems in speech recognition, computational
chemistry, and natural language text processing [PDF]. University of Toronto.
Wang, X. (2017). Deep Learning in Object Recognition, Detection, and Segmentation (4th ed.,
Vol. 8). Boston, MA: Now Publishers.
Ghosh, M., (May 2017). The Business Benefits of Deep Learning, from
http://www.dataversity.net/business-benefits-deep-learning/
Ambati, S., (Nov. 2015). Deep Learning: A brief guide for practical problem solvers from
http://www.infoworld.com/article/3003315/big-data/deep-learning-a-brief-guide-forpractical-problem-solvers.html
Murnane, K., (Apr. 2016). What is Deep Learning and How Is It Useful, from
http://www.forbes.com/sites/kevinmurnane/2016/04/01/what-is-deep-learning-and-howis-it-useful/#464618c010f0 3

The concept of deep learning can be very complex, especially since it involves so much
data. Deep Learning, a branch of Machine Learning, has become one of the largest trends in
algorithmic learning to date. It was inspired by how our brain functions to teach itself when mass
amounts of information are taken in. It holds huge potential in the world of machine problem
solving and addresses the biggest challenge facing programmers today, performance versus data.
As access to data has increased the performance of machines and their capability to handle such
large amounts of input has become challenging to say the least! Through Deep Learning
techniques programmers have been able to increase the amount of data machines can take, all
while achieving better performance. Even though Deep Learning has been around since the
1950’s, it was not fully utilized until recently. Businesses from around the world have begun
investing in systems that utilize Deep Learning methodologies to better solve complex problems
of mass data. Companies such as Google, Apple, and Facebook are particularly interested in
utilizing Deep Learning to achieve technological prowess over their competition. Deep Learning
has become so prolific that individual people are now discovering that it is a strong tool for
researching and discovering new medicines, making financial decisions, as well as solving
complex issues in transportation. In this paper we will seek to generate conclusions regarding
Deep Learning and explore the various ways it has begun to be implemented. Deep Learning is comprised of various applications that may applied including image
recognition, object recognition, speech recognition, and traditional classification methods. The
combinations of these applications will simplify the tasks that must be performed by computers
in order to put out the solution or answer being searched for by the user. Originally we would
write programs for computers to follow, but what if there is an error in the information that
entered? How will the computer then know what to do? In order for these tasks to be completed
algorithms must be developed that will enable a computer to perform the necessary steps in
solving the answer. These algorithms will allow our computers to oversee thousands of examples
with the correct solutions and from them generate new solutions. Blog, G. (Ed.). (2015, September 5). Building a Deeper Understanding of Images. Retrieved
January 29, 2017, from https://research.googleblog.com/2014/09/building-deeperunderstanding-of-images.html
Dahl, G. E. (n.d.). Deep learning approaches to problems in speech recognition, computational
chemistry, and natural language text processing [PDF]. University of Toronto.
Wang, X. (2017). Deep Learning in Object Recognition, Detection, and Segmentation (4th ed.,
Vol. 8). Boston, MA: Now Publishers.

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