Large Batch Optimization for Deep Learning: Training BERT in 76 minutesYang YouJing LiSashank ReddiJonathan HseuSanjiv KumarSrinadh BhojanapalliXiaodan SongJames DemmelKurt KeutzerCho-Jui HsiehInternational Conference on Learning Representations
不要小看 optimization 这个问题,有时候就算你的 error surface 是 convex 碗状都不见得很好 train。如果我们采用固定的 learning rate,一般很难得到好的结果,所以才需要adaptive learning rate、 Adam 等改进过的 optimization 的方法。 Batch Normalization 则是通过直接改变 input feature 的分布,得到一张均匀、光滑...
（4）为什么在Batch Normalization中引入gamma和beta？（5）为什么Batch Normalization可以防止过拟合？（6）...
Optimization algorithms(优化算法)---deeplearning.ai---笔记(17) {{\rm{v}}_{db}}\end{array}$$ 其中alpha和beta为超参数,beta取值一般为0.9(2)RMSprop $$\begin{array}{l}{s_{dW...{db}} + \varepsilon } }}\end{array}$$ 其中alpha和beta2为超参数. (3)Adam $$\begin{array}{l}{v...
deeplearning.ai - 机器学习策略 (2) 结构化机器学习项目 吴恩达 Andrew Ng Error Analysis Carrying out error analysis 误差分析 look at dev examples to evaluate ideas evaluate multiple ideas in parallel Cleaning up incorrectly labeled data If the errors ar......
optimizer—which are typical hyperparameter choices in deep learning37. We benchmarked the four implemented optimization algorithms: SGD, SGD with momentum, RMSProp, and Adam (details are given in the Methods section). To assess the impact of the learning rate, we considered four learning rate ...
Optimization of the KL divergence loss: We jointly optimize the cluster centers \(\{ \mu _j:j = 1, \ldots ,K\}\) and the deep neural network parameters using stochastic gradient descent. The gradients of \(L\) with respect to feature space embedding of each data point \(z_i\) and...
p=5 笔记参照Github: https:///unclestrong/DeepLearning_LHY21_Notes 1、局部最小值与鞍点 1.1 Critical Point 在做Optimization的时候有时我们会发现,随著参数不断的更新,到一定程度不管怎样update参数,loss都掉不下去。这到底是为什么? 这是其实就是因为我们现在走到了一个参数对loss的微分为零...
吴恩达《深度学习》-第二门课 (Improving Deep Neural Networks:Hyperparameter tuning, Regularization and Optimization)-第三周:超参数调试 、 Batch 正则化和程序框架(Hyperparameter tuning)-课程笔记 第三周:超参数调试 、 Batch 正则化和程序框架(Hyperparameter tuning)...
* non-convex optimization(training the network): f_i:loss function, x: weights ### SGD and its variants iteratively taking steps of the form: 相当于带noise的梯度下降。 一般来说 * pros:(a)对于强的凸函数能收敛到极小值,对于非凸函数能收敛到驻点(stationary points);(b)能够避免走到鞍点(saddl...