Whatever Deep Learning Framework you feel most comfortable, choose one! I would go for either PyTorch or Keras as they are both relatively easy to pick up and have a very active community. Reflect After completing your project, take a day or so to reflect on what you have achieved, what ...
deep learning makes it easy to increase model complexity to make a more efficient use of massive data. At the same time, studies have shown that the accuracy of deep learning models can increase with a larger size of data. As the field of MRC continues to evolve, more and more datasets ...
Deep Learning Model Convertors Note: This is not one convertor for all frameworks, but a collection of different converters. Because github is an open source platform, I hope we can help each other here, gather everyone's strength. Because of these different frameworks, the awesome convertors ...
We have used the following search queries which were chosen by experts in the field for search criteria in this review paper which are (“Deep Learning”), (“Data scarcity”), (“Convolutional Neural Network”), (“Deep Learning” AND “Architectures”), (“Deep Learning”) AND (“learnin...
Table 3.Loss functions of commonly used deep learning models. Empty CellNameEquationVariable definition Image classificationCross-Entropyl(y,y^)=−∑inyilogy^i•nnumber of classes •yis ground truth (GT) classes Binary cross-entropy(log loss)l(y,y^)=−(ylog(y^)+(1−y)log(1−...
As these algorithms developed, they could tackle many problems. But some things that humans found easy (like speech or handwriting recognition) were still hard for machines. However, if machine learning is about mimicking how humans learn, why not go all the way and try to mimic the human br...
Easy reproduction Due to the lack of a publicly available codebase and variances in programming languages, the diversity and complexity of deep learning methods make it difficult for researchers to reproduce the results from the original papers. Another reason specifically for deep learning approaches ...
In the last few years, the deep learning (DL) computing paradigm has been deemed the Gold Standard in the machine learning (ML) community. Moreover, it has gradually become the most widely used computational approach in the field of ML, thus achieving outstanding results on several complex cog...
Ever-growing demand for artificial intelligence has motivated research on unconventional computation based on physical devices. While such computation devices mimic brain-inspired analog information processing, the learning procedures still rely on metho
Implementation of popular deep learning networks with TensorRT network definition API - wang-xinyu/tensorrtx