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Showing posts with label deep learning. Show all posts
Showing posts with label deep learning. Show all posts

Monday, September 13, 2021

Hebrew University of Jerusalem, Israel - New Study Finds a Single Neuron Is a Surprisingly Complex Little Computer

Title:
New Study Finds a Single Neuron Is a Surprisingly Complex Little Computer
 
Author:
Jason Dorrier
 
Published:
SingularityHub, 12 September 2021
 
From the article:
Scientists know biological neurons are more complex than the artificial neurons employed in deep learning algorithms, but it’s an open question just how much more complex.  
 
In a fascinating paper published recently in the journal Neuron, a team of researchers from the Hebrew University of Jerusalem tried to get us a little closer to an answer. While they expected the results would show biological neurons are more complex—they were surprised at just how much more complex they actually are.
 

Friday, July 3, 2020

Scholarly Article (IJIET, 2020) - Efficiency Assessment of Undergraduate Students Based on Academic Record Using Deep Learning Methodology

Title:
Efficiency Assessment of Undergraduate Students Based on Academic Record Using Deep Learning Methodology

Author:
Arthit Buranasing

Published:
International Journal of Information and Education Technology (IJIET), 2020, Volume 10(7): 511-515.
http://www.ijiet.org/show-142-1650-1.html

Abstract:
Computer science is the study of computers and computational systems which computer scientists deal mostly with application software and system software. Although knowing how to program is essential to the study of computer science, but it is only one element of the field. For example, software development uses various skills and techniques which are included in various subjects of a general computer science course. This paper focuses on senior students in computer science course who would like to assess the efficiency of their computer science skill in order to improve themselves. Moreover, the model also helps in the recruitment of new staff so that the companies would be able to assess the efficiency of newly graduated students or inexperienced candidates. This is because the lack of skill and inefficiency could cause problems to the hiring companies since they would have to invest time and money into training the new staff. This model can solve this problem by evaluating the performance and define the skills that must be improved directly. The result of the model is satisfactory, the average accuracy from experiment testing of confusion matrix is 89.33%.

Tuesday, April 28, 2020

Short Article - Springer has released 65 Machine Learning and Data books for free

Title:
Springer has released 65 Machine Learning and Data books for free

Author:
Uri Eliabayev

Published:
Towards Data Science, 26 April 2020

From the article:
"Springer has released hundreds of free books on a wide range of topics to the general public. The list, which includes 408 books in total, covers a wide range of scientific and technological topics. In order to save you some time, I have created one list of all the books (65 in number) that are relevant to the data and Machine Learning field."

To read the article & see the 65 books mentioned:
https://towardsdatascience.com/springer-has-released-65-machine-learning-and-data-books-for-free-961f8181f189