François Chollet
François Chollet
Verified email at google.com
Title
Cited by
Cited by
Year
keras
F Chollet
7905*2015
Xception: Deep learning with depthwise separable convolutions
F Chollet
Proceedings of the IEEE conference on computer vision and pattern …, 2017
28272017
Deep Learning with Python
F Chollet
Manning Publications, 2017
1128*2017
Tensor2tensor for neural machine translation
A Vaswani, S Bengio, E Brevdo, F Chollet, AN Gomez, S Gouws, L Jones, ...
arXiv preprint arXiv:1803.07416, 2018
2472018
Deep Learning with R
F Chollet, JJ Allaire
Manning Publications, 2018
150*2018
Deepmath-deep sequence models for premise selection
G Irving, C Szegedy, AA Alemi, N Eén, F Chollet, J Urban
Advances in Neural Information Processing Systems, 2235-2243, 2016
1192016
others. Keras; 2015
F Chollet
XH Lu et al./Application of Machine Learning and Grocery Transaction Data 252, 2018
572018
Building powerful image classification models using very little data
F Chollet
Keras Blog, 2016
562016
Holstep: A machine learning dataset for higher-order logic theorem proving
C Kaliszyk, F Chollet, C Szegedy
arXiv preprint arXiv:1703.00426, 2017
482017
Building autoencoders in keras
F Chollet
The Keras Blog, 2016
342016
On the Measure of Intelligence
F Chollet
arXiv preprint arXiv:1911.01547, 2019
172019
Information-theoretical label embeddings for large-scale image classification
F Chollet
arXiv preprint arXiv:1607.05691, 2016
132016
The limitations of deep learning
F Chollet
Deep Learning With Python, 2017
62017
Introduction to Keras
F Chollet
March 9th, 2018
32018
The future of deep learning
F Chollet
future 8, 2, 2017
32017
A Definition of Intelligence for the Real World
F Chollet
Journal of Artificial General Intelligence 11 (2), 27-30, 2020
22020
Deep learning v jazyku Python: knihovny Keras, Tensorflow
F Chollet
Grada Publishing, 2019
22019
Depthwise separable convolutions for neural machine translation
AN Gomez, LM Kaiser, F Chollet
US Patent App. 16/688,958, 2020
12020
Classifying images using machine learning models
F Chollet
US Patent App. 16/317,763, 2019
2019
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Articles 1–19