Designing Optimizer for Deep Learning (Record no. 16588)
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000 -LEADER | |
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fixed length control field | 01648nam a22001697a 4500 |
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
Classification number | 006.31 |
Item number | M476D |
100 ## - MAIN ENTRY--AUTHOR NAME | |
Personal name | Mazumder, Sudipta |
245 ## - TITLE STATEMENT | |
Title | Designing Optimizer for Deep Learning |
Statement of responsibility, etc | by Sudipta Mazumder |
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
Place of publication | IIT Jodhpur |
Name of publisher | Department of Computer Science and Technology |
Year of publication | 2023 |
300 ## - PHYSICAL DESCRIPTION | |
Number of Pages | vii,10p. |
Other physical details | HB |
500 ## - GENERAL NOTE | |
General note | Optimizers form the backbone of any convolutional network as they are responsible for making the functions converge faster. The optimizers do this by modifying the weights and learning rate of the algorithm, which reduces the loss and improves the accuracy. A lot of optimizers have gained traction over the years, out of which SGD and Adam take the cake. Adam has taken the lead as it helps to reduce the dying gradient problem of SGD. However, we still have scope for improvement. With this paper, we aim to introduce a new algorithm that surpasses the performance of Adam by calculating the angular gradients (cosine and tangent angles) at consecutive steps. This algorithm uses the gradient of the current step, the previous step, and the step previous to that. As we present more information to the optimizer’s algorithm, the algorithm has more information, making it better poised to make more accurate predictions at faster convergence rates. We have tested this approach on benchmark datasets and compared it with other state-of-the-art optimizers, and have obtained superior results in almost every approach.<br/> |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical Term | Department of Computer Science and Technology |
Topical Term | Optimizers |
Topical Term | MTech Theses |
700 ## - ADDED ENTRY--PERSONAL NAME | |
Personal name | Paul, Angshuman |
942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
Koha item type | Thesis |
Withdrawn status | Lost status | Damaged status | Not for loan | Collection code | Permanent Location | Current Location | Shelving location | Date acquired | Source of acquisition | Full call number | Accession Number | Price effective from | Koha item type |
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Theses | S. R. Ranganathan Learning Hub | S. R. Ranganathan Learning Hub | Reference | 2024-04-01 | Office of Academics | 006.31 M476D | TM00524 | 2024-07-01 | Thesis |