Matches in SemOpenAlex for { <https://semopenalex.org/work/W2966308027> ?p ?o ?g. }
Showing items 1 to 77 of
77
with 100 items per page.
- W2966308027 abstract "Abstract Deep learning uses multiple layers of artificial neurons for classification and pattern recognition. The biggest drawbacks of deep learning algorithms have been the high computation cost, inter-processor communication bottlenecks and parameters training time. Hence, incorporating parallel computing into deep learning decreases the computation time of complex deep learning algorithms. This chapter presents how parallelization is applied over many processors which are loosely coupled. Up to 4096 processes are scaled linearly with higher accuracy and zero loss percentage. This capacity of huge scaling helps in training billions of training examples in just a few hours. Various applications of Hessian-free parallelization mechanism on bioinformatics applications are in gene therapy, drug development, antibiotic resistance research, waste cleanup, climate change studies, bioweapon creation, improving nutritional quality and veterinary science." @default.
- W2966308027 created "2019-08-13" @default.
- W2966308027 creator A5011020753 @default.
- W2966308027 creator A5085870727 @default.
- W2966308027 date "2019-01-01" @default.
- W2966308027 modified "2023-09-23" @default.
- W2966308027 title "Parallel Machine Learning and Deep Learning Approaches for Bioinformatics" @default.
- W2966308027 cites W1019830208 @default.
- W2966308027 cites W2011582941 @default.
- W2966308027 cites W2076063813 @default.
- W2966308027 cites W2081310300 @default.
- W2966308027 cites W2111841128 @default.
- W2966308027 cites W2160815625 @default.
- W2966308027 cites W2171860505 @default.
- W2966308027 cites W2264017649 @default.
- W2966308027 cites W2517582793 @default.
- W2966308027 cites W2561981131 @default.
- W2966308027 cites W2582187633 @default.
- W2966308027 cites W2747811776 @default.
- W2966308027 cites W2749122933 @default.
- W2966308027 cites W2804025582 @default.
- W2966308027 cites W2804672687 @default.
- W2966308027 cites W2906946803 @default.
- W2966308027 cites W2913682694 @default.
- W2966308027 cites W2919115771 @default.
- W2966308027 cites W2919709896 @default.
- W2966308027 cites W2936111010 @default.
- W2966308027 cites W2950374603 @default.
- W2966308027 doi "https://doi.org/10.1016/b978-0-12-816718-2.00022-1" @default.
- W2966308027 hasPublicationYear "2019" @default.
- W2966308027 type Work @default.
- W2966308027 sameAs 2966308027 @default.
- W2966308027 citedByCount "2" @default.
- W2966308027 countsByYear W29663080272020 @default.
- W2966308027 countsByYear W29663080272021 @default.
- W2966308027 crossrefType "book-chapter" @default.
- W2966308027 hasAuthorship W2966308027A5011020753 @default.
- W2966308027 hasAuthorship W2966308027A5085870727 @default.
- W2966308027 hasConcept C108583219 @default.
- W2966308027 hasConcept C11413529 @default.
- W2966308027 hasConcept C119857082 @default.
- W2966308027 hasConcept C154945302 @default.
- W2966308027 hasConcept C173608175 @default.
- W2966308027 hasConcept C2524010 @default.
- W2966308027 hasConcept C33923547 @default.
- W2966308027 hasConcept C41008148 @default.
- W2966308027 hasConcept C45374587 @default.
- W2966308027 hasConcept C83283714 @default.
- W2966308027 hasConcept C99844830 @default.
- W2966308027 hasConceptScore W2966308027C108583219 @default.
- W2966308027 hasConceptScore W2966308027C11413529 @default.
- W2966308027 hasConceptScore W2966308027C119857082 @default.
- W2966308027 hasConceptScore W2966308027C154945302 @default.
- W2966308027 hasConceptScore W2966308027C173608175 @default.
- W2966308027 hasConceptScore W2966308027C2524010 @default.
- W2966308027 hasConceptScore W2966308027C33923547 @default.
- W2966308027 hasConceptScore W2966308027C41008148 @default.
- W2966308027 hasConceptScore W2966308027C45374587 @default.
- W2966308027 hasConceptScore W2966308027C83283714 @default.
- W2966308027 hasConceptScore W2966308027C99844830 @default.
- W2966308027 hasLocation W29663080271 @default.
- W2966308027 hasOpenAccess W2966308027 @default.
- W2966308027 hasPrimaryLocation W29663080271 @default.
- W2966308027 hasRelatedWork W2567271240 @default.
- W2966308027 hasRelatedWork W2795261237 @default.
- W2966308027 hasRelatedWork W3009238340 @default.
- W2966308027 hasRelatedWork W3047894882 @default.
- W2966308027 hasRelatedWork W3099765033 @default.
- W2966308027 hasRelatedWork W3136979370 @default.
- W2966308027 hasRelatedWork W3215138031 @default.
- W2966308027 hasRelatedWork W4223943233 @default.
- W2966308027 hasRelatedWork W4225161397 @default.
- W2966308027 hasRelatedWork W4250304930 @default.
- W2966308027 isParatext "false" @default.
- W2966308027 isRetracted "false" @default.
- W2966308027 magId "2966308027" @default.
- W2966308027 workType "book-chapter" @default.