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Human Intelligence
Artificial Intelligence is a human creation, it surely destroys human.
Robustness of Models
Classic Models
Not Supervised Learning
Sequence and NLP
Transformer and its Variants
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我的 Linux Kerenl 學習路程
我的 Linux Kerenl 學習路程
也算是半個文組翻身吧
Guan
Jul 21
Review: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Review: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
Introduction to Vision Transformer
Guan
Aug 28, 2022
Notes of Clean Code from Uncle Bob
這是一篇不完整的 Clean Code — Uncle Bob 速記,內文請與影片搭配使用。
Guan
Dec 30, 2021
Speed up Python numerical computation 660,000 times with Numba
Speed up Python numerical computation 660,000 times with Numba
Python 在現代科學運算上扮演很重要的角色,尤其是 Machine Learning 領域,其核心是一連串的矩陣運算和最佳化理論;但受限於其 Interpreter GIL 和 動態類型語言等等,在中大型運算上的效能一直為人所詬病,所以 Numpy 應運而生,大部分使用 C…
Guan
Aug 30, 2021
Review: Meta Pseudo Labels — Series 3 of 3
Review: Meta Pseudo Labels — Series 3 of 3
這是 Google Self-training 系列的最後一篇,這個系列第一篇提出了一個新的Teacher-student 的訓練架構:加入適當的噪聲後,在不斷的迭代訓練下,能夠不斷的推昇準確度。
Guan
Jun 23, 2021
Review: Rethinking Pre-training and Self-training — Series 2 of 3
Review: Rethinking Pre-training and Self-training — Series 2 of 3
Pre-training 一直是 Computer Vision 愛用的技術,一般認為它能夠非常快的配適在不同資料集(以及任務)上,早年甚至認為它在任何條件下都能夠快速的達到與 train-from-scratch 相等的準確度,甚至認為在相同的 iteration…
Guan
Feb 27, 2021
Review: Self-training with Noisy Student improves ImageNet classification — Series 1 of 3
Review: Self-training with Noisy Student improves ImageNet classification — Series 1 of 3
Introduction to noisy student
Guan
Feb 15, 2021
Latest
How does Batch Normalization REALLY Work?(It's not about Internal Variate Shift)
How does Batch Normalization REALLY Work?(It's not about Internal Variate Shift)
How Does Batch Normalization Help Optimization?
Guan
Nov 16, 2020
Review: Attention is all you need
Review: Attention is all you need
Introduction to Self-Attention and Transformer
Guan
Oct 8, 2020
Review a series of Semi-Supervised Learning algorithms: MixMatch, ReMixMatch and FixMatch
Review a series of Semi-Supervised Learning algorithms: MixMatch, ReMixMatch and FixMatch
Introduction to Semi-Supervised
Guan
Jul 6, 2020
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