CS231n:(4) Backpropagation and Neural Networks
問答總結: 計算圖的組成部分有哪些? 計算圖反向傳播時變量的梯度計算由哪兩部分組成? 各種常見門的稱號是什麼，爲什麼有這樣的稱號? 當一個變量流向多個分支時，應該怎樣計算它的梯度? 當變量向量化時，相當於每個輸入向量的變量流向了多個分支，本地梯度保存的是雅可比矩陣，它的維度是怎樣的。 當實現計算圖時，是怎樣實現的，有哪兩個重要的類，它們有哪兩個重要的函數，分別都在幹嘛。 如何解釋多層神經網絡對單層
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相關文章
- 1. 【CS224n】Neural Networks, Backpropagation
- 2. Neural Networks: Learning: Backpropagation intuition
- 3. CS231n 2017Spring Lecture4 Backpropagation and Neural Networks 總結
- 4. Multilayer Neural Networks and the Backpropagation Learning Algorithm
- 5. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 6. (zhuan) How to Train Neural Networks With Backpropagation
- 7. Artificial Neural Networks: Mathematics of Backpropagation (Part 4)
- 8. Recurrent Neural Networks Tutorial, Part 3 – Backpropagation Through Time and Vanishing Gradients
- 9. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 10. CS231n_Lecture4_介紹神經網絡（Backpropagation、Neural Networks）