http://www.kdd.org/kdd2016/papers/files/rfp0697-chenAemb.pdf https://homes.cs.washington.edu/~tqchen/pdf/BoostedTree.pdf [Training Loss measures how well model fit on training data] [Regularization, measures complexity of model] [simple] [predictive]…
The biggest difference between LES and RANS is that, contrary to LES, RANS assumes that \(\overline{u'_i} = 0\) (see the Reynolds-averaged Navier–Stokes equations). In LES the filter is spatially based and acts to reduce the amplitude of the scales o…
I have finished the first course in the DeepLearnin.ai series. The assignment is relatively easy, but it indeed provides many interesting insight. You can find some summary notes of the first course in my previous 2 posts. sigmoid and shallow NN Forw…
About this Course This course will teach you the "magic" of getting deep learning to work well. Rather than the deep learning process being a black box, you will understand what drives performance, and be able to more systematically get good res…
Normalization Normalization refers to rescaling real valued numeric attributes into the range 0 and 1. It is useful to scale the input attributes for a model that relies on the magnitude of values, such as distance measures used in k-nearest neighbor…
3. Bayesian statistics and Regularization Content 3. Bayesian statistics and Regularization. 3.1 Underfitting and overfitting. 3.2 Bayesian statistics and regularization. 3.3 Optimize Cost function by regularization. 3.3.1 Regularized linear regressi…
声明:所有内容来自coursera,作为个人学习笔记记录在这里. Regularization Welcome to the second assignment of this week. Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem, if the training dataset is not big enough. Sure it do…
Regularization method(正则化方法) Outline Overview of Regularization L0 regularization L1 regularization L2 regularization Elastic Net regularization L2,1 regularization Model example Reference Overview of Regularization Main goal: 1. Prevent over-fitting…
Regularization Deep Learning models have so much flexibility and capacity that overfitting can be a serious problem,if the training dataset is not big enough. Sure it does well on the training set, but the learned network doesn't generalize to new ex…
machine learning(13) --Regularization:Regularized linear regression Gradient descent without regularization with regularization θ0与原来是的没有regularization的一样 θ1-n和原来相比会稍微变小(1-αλ⁄m)<1 Normal equation without regular…
不同的λ(0,1,10,100)值对regularization的影响\ 预测新的值和计算模型的精度 %% ============= Part 2: Regularization and Accuracies =============% Optional Exercise:% In this part, you will get to try different values of lambda and % see how regularization affects the decisio…
梯度修剪 梯度修剪主要避免训练梯度爆炸的问题,一般来说使用了 Batch Normalization 就不必要使用梯度修剪了,但还是有必要理解下实现的 In TensorFlow, the optimizer’s minimize() function takes care of both computing the gradients and applying them, so you must instead call the optimizer’s compute_gradients()…
The Model Complexity Myth (or, Yes You Can Fit Models With More Parameters Than Data Points) An oft-repeated rule of thumb in any sort of statistical model fitting is "you can't fit a model with more parameters than data points". This idea appea…
Generalized linear models with nonlinear feature transformations (特征工程 + 线性模型) are widely used for large-scale regression and classification problems with sparse inputs. Memorization of feature interactions (线性模型中学习到的特征系数解释性强)through a wide set of cr…
假设我们现在想要知道what degree of polynomial to fit to a data set 或者 应该选择什么features 或者 如何选择regularization parameter λ 我们该如何做?----Model selection process 很好的拟合training set并不意味着是一个好的hypothesis 上图是一个overfitting的例子,它能很好的拟合training data,但它不是一个好的预测函数.所以一般来说,the tra…
Day 1: Setting up ROS: Indigo OS: Ubuntu 14.04 OS: Gazebo 7.0.0 Initialize the workspace To create the basic skeleton of the directory structure, we begin with a workspace {WORKSPACE}_ws, where we set {WORKSPACE}=mybot. cd ~ mkdir -p mybot_ws/src cd…
