This Major Qualifying Project is an open source supervised machine learning neural network software development kit (SDK) utilizing a stochastic gradient descent with cross-entropy error backpropagation algorithm, implemented in Java. This SDK provides a human-readable implementation of a commonly used method of machine learning, thereby supplying a transparent learning tool for students. The SKD was demonstrated by providing functional examples of networks designed for classification. Examples were provided ranging in complexity from the simplest most transparent example of learning, to a real world example of classification based on color
This work explores the impact of various design and training choices on the resilience of a neural n...
The thesis tries to investigate on how a machine learning tool can be used to achieve performance pr...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
In many machine learning applications, interpretability is of the utmost importance. Artificial inte...
Honors (Bachelor's)Computer ScienceMathematicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bi...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
Deep neural networks excel at pattern recognition, especially in the setting of large scale supervis...
Interest in artificial neural networks has grown rapidly over the past few years. The technology is ...
Thesis (M.Sc.)-University of Natal, Durban, 1992.Artificial neural networks (ANNs) were originally i...
This paper examines the history and current state of machine learning. It examines neural networks, ...
We have investigated an existing theoretical model for spiking neural networks, and based on this mo...
Thesis (Master of Science in Informatics)--University of Tsukuba, no. 36020, 2016.3.25201
The industrial revolution and the birth of computers has led to a deeper exploration o...
Artificial Neural Networks (ANNs) are complex modelling techniques that can be used to find the rela...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
This work explores the impact of various design and training choices on the resilience of a neural n...
The thesis tries to investigate on how a machine learning tool can be used to achieve performance pr...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...
In many machine learning applications, interpretability is of the utmost importance. Artificial inte...
Honors (Bachelor's)Computer ScienceMathematicsUniversity of Michiganhttp://deepblue.lib.umich.edu/bi...
The basic structure and definitions of artificial neural networks are exposed, as an introduction to...
Deep neural networks excel at pattern recognition, especially in the setting of large scale supervis...
Interest in artificial neural networks has grown rapidly over the past few years. The technology is ...
Thesis (M.Sc.)-University of Natal, Durban, 1992.Artificial neural networks (ANNs) were originally i...
This paper examines the history and current state of machine learning. It examines neural networks, ...
We have investigated an existing theoretical model for spiking neural networks, and based on this mo...
Thesis (Master of Science in Informatics)--University of Tsukuba, no. 36020, 2016.3.25201
The industrial revolution and the birth of computers has led to a deeper exploration o...
Artificial Neural Networks (ANNs) are complex modelling techniques that can be used to find the rela...
An overview of neural networks, covering multilayer perceptrons, radial basis functions, constructiv...
This work explores the impact of various design and training choices on the resilience of a neural n...
The thesis tries to investigate on how a machine learning tool can be used to achieve performance pr...
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 20...