A Survey of the Advances in the Applications of Deep Learning Algorithms Across Different Domains
Bibliographic record
Abstract
Deep learning has revolutionized the modern-day world starting with its application in computer vision such as image classification, face recognition, autonomous vehicle etc. it has been explored in various areas where human beings find it difficult to come up with solutions to the challenges at hand.By the word deep, it implies they are trained with millions, billions of parameters to achieve outstanding results.In this review paper, the fundamentals of deep learning have been discussed extensively starting with the classification, types of activation functions, different deep learning algorithms as well as their applications were also discussed.Recurrent neural network (RNNs) and its variant, convolution neural networks (CNNs) and various architectures, recursive neural networks (RvNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), generative adversarial networks (GANs) and other deep learning were discussed extensively.Some of the findings of researchers for some of these algorithms were highlighted.Based on various paper reviewed and thorough analysis carried out, it was observed that the exploration of deep learnings in this modern-day world has found applications in virtually all fields of life from medicine, academy, transportation, entertainments, particularly the exploration of CNNs, RNNs, and GANs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".