Refty refines each type of deep learning operator with framework-independent logical formulae that describe the computational constraints on both tensors and hyperparameters. Given the neural architecture and h
During the experiment, we first calibrated the performance of the trained deep neural network in each impending failure type. Then, we leveraged the architecture and hyperparameters of the neural network model trained from one type of failure as the pre-trained model for knowledge transfer. The ...
The most significant contribution to our suggested model is establishing a lightweight network in the spectrum of deep learning. Therefore, parallel studies on the existing state-of-the-art models have been described to show the comparison and improved features in this model. In addition, the proc...
The Amazon Resource Name (ARN) of the IAM role associated with the training jobs that the tuning job launches. Returns: (String) #static_hyper_parameters ⇒ Hash<String,String> Specifies the values of hyperparameters that do not change for the tuning job. Returns: (Hash<String,String...
Incrementally learning new information from a non-stationary stream of data, referred to as ‘continual learning’, is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed,...
One of the major advantages of using logistic regression is that there is no hyper-parameter tuning required, thus making it quite efficient. But this doesn't work very well on our data due to the presence of high non-linear structures and logistic regression doesn't perform well in such a...
For a more in-depth look, check out our comparison guides on AI vs machine learning and machine learning vs deep learning. AI refers to the development of programs that behave intelligently and mimic human intelligence through a set of algorithms. The field focuses on three skills: learning,...
Deep learningis an advanced form of machine learning that uses multilayered algorithms called neural networks which simultaneously receive input, process data, and provide output. While deep learning mimics certain processes in the human brain, it doesn’t result in consciousness or reasoning. ...
We include a more detailed description of the optimization hyperparameters, computation infrastructure and convergence criteria used in the development of the model in the section below. Pretraining phase 1. Computation infrastructure: the pretraining of our model was conducted using 16 NVIDIA V100 GPUs...
This study aims to develop a novel deep learning architecture that uses point clouds as input data to classify different types of building roofs. For this purpose, the proposed architecture, whose hyperparameters are optimized using the Bayesian optimization algorithm, is trained and tested on the ...