搜狗深度学习技术在广告推荐领域的应用Skipping Word: A Character-Sequential Representation based Framework for Question Answering. CIKM2016, pages 1869-1872, 2016. Sogou Inc 文本相关性计算 文本相关性计算 深度学习在搜狗搜索广告的一些应用 LSTM LSTM LSTM 中长款 牛仔 外套 ResNet-50层0 码力 | 22 页 | 1.60 MB | 1 年前3- QCon北京2018-《从键盘输入到神经网络--深度学习在彭博的应用》-李碧野Machine Human Machine Human Table Boundary 95% 94% 95% 95% Perfect Table 87% 82% 94% 94% • 48,607 pages evaluated © 2018 Bloomberg Finance L.P. All rights reserved. Ecosystem Modified from https://commons0 码力 | 64 页 | 13.45 MB | 1 年前3
- 《Efficient Deep Learning Book》[EDL] Chapter 6 - Advanced Learning Techniques - Technical Reviewscratch v/s using pre-training strategies. 5 WikiText-103 dataset is derived from English Wikipedia pages. 4 Howard, Jeremy and Sebastian Ruder. "Universal Language Model Fine-tuning for Text Classification0 码力 | 31 页 | 4.03 MB | 1 年前3
- 《Efficient Deep Learning Book》[EDL] Chapter 4 - Efficient Architecturesground-truth labels, which is an example of self-supervised learning using a large dataset like Wikipedia’s pages in English. One of the tasks that we can train the model is to predict a hidden word in a sentence0 码力 | 53 页 | 3.92 MB | 1 年前3
- 《Efficient Deep Learning Book》[EDL] Chapter 3 - Learning Techniquesimage belonging to the given classes. The cat and dog images are sourced from wikipedia Cat and Dog pages under CC BY-SA 3.0 license. They are authored by wikipedia users Joaquim Alves Gaspar and Losch respectively0 码力 | 56 页 | 18.93 MB | 1 年前3
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