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Found 7 papers, 2 papers with code
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.
Comparative Performance Analysis of Neural Networks Architectures on H2O Platform for Various Activation Functions
Deep learning (deep structured learning, hierarchi- cal learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high- level abstractions in data by using multiple processing layers with complex structures or otherwise composed of multiple non-linear transformations.
Performance Analysis of Open Source Machine Learning Frameworks for Various Parameters in Single-Threaded and Multi-Threaded Modes
The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared.
Deep Learning for Fatigue Estimation on the Basis of Multimodal Human-Machine Interactions
The new method is proposed to monitor the level of current physical load and accumulated fatigue by several objective and subjective characteristics.
Chest X-Ray Analysis of Tuberculosis by Deep Learning with Segmentation and Augmentation
Lossless data augmentation of the segmented dataset leads to the lowest validation loss (without overfitting) and nearly the same accuracy (within the limits of standard deviation) in comparison to the original and other pre-processed datasets after lossy data augmentation.
Performance Evaluation of Deep Learning Networks for Semantic Segmentation of Traffic Stereo-Pair Images
Here the results of application of several deep learning architectures (PSPNet and ICNet) for semantic image segmentation of traffic stereo-pair images are presented.
Batch Size Influence on Performance of Graphic and Tensor Processing Units during Training and Inference Phases
The significant speedup was obtained even for extremely low-scale usage of Google TPUv2 units (8 cores only) in comparison to the quite powerful GPU NVIDIA Tesla K80 card with the speedup up to 10x for training stage (without taking into account the overheads) and speedup up to 2x for prediction stage (with and without taking into account overheads).
