Results 51 to 60 of about 2,817,119 (330)
Mapping the evolution of mitochondrial complex I through structural variation
FEBS Letters, EarlyView.Respiratory complex I (CI) is crucial for bioenergetic metabolism in many prokaryotes and eukaryotes. It is composed of a conserved set of core subunits and additional accessory subunits that vary depending on the organism. Here, we categorize CI subunits from available structures to map the evolution of CI across eukaryotes. Respiratory complex I (CI)
Dong‐Woo Shin +2 more
wiley +1 more source
The Programming Historian, 2022 This is the first of a two-part lesson introducing deep learning based computer vision methods for humanities research. Using a dataset of historical newspaper advertisements and the fastai Python library, the lesson walks through the pipeline of ...
Daniel van Strien +4 more
doaj +1 more source
FEBS Letters, EarlyView.Phototrophs evolved light‐harvesting systems adapted for efficient photon capture in habitats enriched in far‐red radiation. A subset of eukaryotic pigment‐binding proteins can absorb far‐red photons via low‐energy chlorophyll states known as red forms.
Antonello Amelii +8 more
wiley +1 more source
The Programming Historian, 2022 This is the second of a two-part lesson introducing deep learning based computer vision methods for humanities research. This lesson digs deeper into the details of training a deep learning based computer vision model.
Daniel van Strien +4 more
doaj +1 more source
Deep Learning At Scale and At Ease
, 2016 Recently, deep learning techniques have enjoyed success in various multimedia applications, such as image classification and multi-modal data analysis. Large deep learning models are developed for learning rich representations of complex data.
Beng Chin Ooi +8 more
core +1 more source
Deep learning and geometric deep learning: An introduction for mathematicians and physicists
International Journal of Geometric Methods in Modern Physics, 2023 In this expository paper, we want to give a brief introduction, with few key references for further reading, to the inner functioning of the new and successful algorithms of Deep Learning and Geometric Deep Learning with a focus on Graph Neural Networks.
R. Fioresi, F. Zanchetta
openaire +3 more sources
Molecular Oncology, EarlyView.This study investigates gene expression differences between two major pediatric acute lymphoblastic leukemia (ALL) subtypes, B‐cell precursor ALL, and T‐cell ALL, using a data‐driven approach consisting of biostatistics and machine learning methods. Following analysis of a discovery dataset, we find a set of 14 expression markers differentiating the ...
Mona Nourbakhsh +8 more
wiley +1 more source
Encyclopedia with Semantic Computing and Robotic Intelligence, 2017 Seminar about deep learning and ...
Arjona, Gorchs, , Hernández-Ramírez
openaire +2 more sources
Deep Learning for Forecasting Stock Returns in the Cross-Section
, 2018 Many studies have been undertaken by using machine learning techniques, including neural networks, to predict stock returns. Recently, a method known as deep learning, which achieves high performance mainly in image recognition and speech recognition ...
A Subrahmanyam +12 more
core +1 more source
, 2018 Deep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains ...
Stefan Lörwald +10 more
openaire +3 more sources