Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Anthony L. Caterini"'
Autor:
Anthony L. Caterini, Dong Eui Chang
Publikováno v:
Deep Neural Networks in a Mathematical Framework ISBN: 9783319753034
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c413f0eead36d5e0477364911623eb40
https://doi.org/10.1007/978-3-319-75304-1_6
https://doi.org/10.1007/978-3-319-75304-1_6
Autor:
Anthony L. Caterini, Dong Eui Chang
Publikováno v:
Deep Neural Networks in a Mathematical Framework ISBN: 9783319753034
In the previous chapter, we took the first step towards creating a standard mathematical framework for neural networks by developing mathematical tools for vector-valued functions and their derivatives. We use these tools in this chapter to describe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::14de633888177ccb5f43bdd91aaf8770
https://doi.org/10.1007/978-3-319-75304-1_3
https://doi.org/10.1007/978-3-319-75304-1_3
Autor:
Anthony L. Caterini, Dong Eui Chang
Publikováno v:
Deep Neural Networks in a Mathematical Framework ISBN: 9783319753034
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ccce5baa02e7af6dd4a11eae29a161e9
https://doi.org/10.1007/978-3-319-75304-1_1
https://doi.org/10.1007/978-3-319-75304-1_1
Autor:
Anthony L. Caterini, Dong Eui Chang
Publikováno v:
Deep Neural Networks in a Mathematical Framework ISBN: 9783319753034
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67f8bb2af3a7b5750f059a4b6e0b603a
https://doi.org/10.1007/978-3-319-75304-1_2
https://doi.org/10.1007/978-3-319-75304-1_2
Autor:
Anthony L. Caterini, Dong Eui Chang
Publikováno v:
SpringerBriefs in Computer Science ISBN: 9783319753034
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b75b034207062967c6e3d52f54b6b5c3
https://doi.org/10.1007/978-3-319-75304-1
https://doi.org/10.1007/978-3-319-75304-1
Autor:
Dong Eui Chang, Anthony L. Caterini
Publikováno v:
Deep Neural Networks in a Mathematical Framework ISBN: 9783319753034
We developed an algebraic framework for a generic layered network in the preceding chapter, including a method to express error backpropagation and loss function derivatives directly over the inner product space in which the network parameters are de
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::101aa7a35477a015e537d07b65ef27f7
https://doi.org/10.1007/978-3-319-75304-1_4
https://doi.org/10.1007/978-3-319-75304-1_4
Publikováno v:
ICPADS
Collaborative filtering algorithms are important building blocks in many practical recommendation systems. For example, many large-scale data processing environments include collaborative filtering models for which the Alternating Least Squares (ALS)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::761f094576ba1abcda69c6c9e1e75090
https://doi.org/10.1109/icpads.2015.91
https://doi.org/10.1109/icpads.2015.91
Autor:
Vaheb, Hamed
This thesis serves three primary purposes, first of which is to forecast two stocks, i.e. Goldman Sachs (GS) and General Electric (GE). In order to forecast stock prices, we used a long short-term memory (LSTM) model in which we inputted the prices o
Externí odkaz:
http://arxiv.org/abs/2010.06417
Dimensionality reduction, also known as manifold learning, is an area of machine learning used for extracting informative features from data for better representation of data or separation between classes. This book presents a cohesive review of line