The first edition of this textbook was published in 2021. Over the past two years, we have invested in enhancing all aspects of deep learning methods to ensure the book is comprehensive and impeccable. Taking into account feedback from our readers and audience, the author has diligently updated...
data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing...
This Series publishes books on all aspects of computational methods used in engineering and the sciences. With emphasis on simulation through mathematical modelling, the Series accepts high quality content books across different domains of engineering, materials, and other applied sciences. The Series pub...
The use of computational methods and tools to deepen our understanding of long-standing questions in the social sciences has been rapidly growing in recent years. This Collection includes manuscripts published by Nature Computational Science – from research papers to Review articles and opinion pieces ...
Genki Yagawa& Atsuya Oishi Part of the book series:Lecture Notes on Numerical Methods in Engineering and Sciences((LNNMES)) 1341Accesses Abstract Since the deep learning is now a hot topic in computational mechanics with neural networks and many related studies have been reported recently, we dis...
Advanced machine learning and network methods can be introduced to investigate more complex and hidden structures within the data and create big value out of the data. For example, deep learning has shown great promise in business and computer sciences, bu...
efficiency. For example, how parallel the system setup is; what architecture model has(e.g. group convolution costs in MACs); what computing platform the model uses(e.g. Cudnn has GPU acceleration for deep neural networks and standard operations such as forward or normalization are highly ...
第一篇是2016年Molecular System Biology的文章,题目是Deep learning for computational biology,题目很大,但是它主要只讲了调控基因组(regulatory genomics)和生物图像分析两部分内容。正巧,第二篇是即将在Bioinformatics见刊的文章,题目是An introduction to deep learning on biological sequence data -- examples and ...
Computational Chemistry offers accurate physics-based simulation methods to assist in drug discovery and protein modeling problems. The associated computational costs grow significantly with a number of atoms. Machine learning algorithms solve this problem by accelerating computations while preserving accuracy....
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we report an interpretable weakly supervised deep-learning...