CS231n Lecture 15 | Efficient Methods and Hardware for Deep Learning
Lecture 15主要從算法和硬件兩個層面講解了模型壓縮和優化等問題，以實現深度學習模型的體積減少、參數數量縮減、計算量減少、計算加速等。 目前深度學習模型存在的幾大問題。 降低能耗是很重要的，那麼這些能量都消耗在哪了呢？ 上圖可以看到存儲訪問的耗能比數學運算的耗能高兩到三個數量級，因此，我們需要將算法和硬件聯動設計來實現效能的提升。 我們將從以下四個方面來講解： 在此之前，我們先來將一些硬件的
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相關文章
- 1. 【韓鬆博士畢業論文】Efficient methods and hardware for deep learning
- 2. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Learning both Weights and Connections 》
- 3. 【論文閱讀】韓鬆《Efficient Methods And Hardware For Deep Learning》節選《Deep compression》
- 4. 韓鬆博士畢業論文Efficient methods and hardware for deep learning論文詳解
- 5. An Efficient Deep Learning Approach for Collaborative Filtering Recommender System
- 6. Some methods of deep learning and dimensionality reduction
- 7. CS231n lecture 3 Linear classification and Optimization
- 8. 【5分鐘 Paper】Asynchronous Methods for Deep Reinforcement Learning
- 9. 論文閱讀15：Massively Parallel Methods for Deep Reinforcement Learning
- 10. CS224d: Deep Learning for Natural Language Process