MSE ▪ Cross Entropy Loss ▪ Hinge Loss Entropy ▪ Uncertainty ▪ measure of surprise ▪ higher entropy: higher uncertainty. Claude Shannon https://towardsdatascience.com/demystifying-cross-entropy-e80e3ad54a8 -e80e3ad54a8 Lottery Cross Entropy ▪ P=Q ▪ cross Entropy = Entropy ▪ for one-hot encoding, ▪ entropy = 1log1=0 ? ?, ? = − ෍ ?(?) log ?(?) KL Divergence Binary Classification for example why

preprocessing layer at the bottom (right after the input layer). We compile the model with a sparse cross entropy loss function (discussed in chapter 2) and the adam optimizer. from tensorflow.keras import student more information than just hard binary labels. The student is trained using the regular cross-entropy loss with the hard labels, as well as using the distillation loss function which uses the function that minimizes the cross-entropy for both soft and hard labels. The combined loss function is as follows: In the above equation, denotes the original loss function (cross-entropy) that uses the

Negative pairs are created using all the other pairs, and the loss to be minimized is a variant of the cross-entropy loss. We would refer you to the SimCLR paper for more details about the chosen loss functions smoothing is easy to implement on your own. However, various frameworks support it through their cross entropy loss function implementation. For example, Tensorflow provides a parameter to set the via Adding a ‘subclass head’ which generates subclasses for each original class. 2. Using the original cross entropy loss where the probability of a class is calculated by summing up the probabilities of all

Kaggle Train Set Test Set Val Set Unavailable train-val-test K-fold cross-validation Train Set Test Set Val Set k-fold cross validation ▪ merge train/val sets ▪ randomly sample 1/k as val set

they can run on GPU. • Examples: And more operations like: Indexing, slicing, reshape, transpose, cross product, matrix product, element wise multiplication etc... Tensor (continued) • Attributes of performs the updates Loss • Loss • Various predefined loss functions to choose from • L1, MSE, Cross Entropy …... Model • In PyTorch, a model is represented by a regular Python class that inherits

L等 • Optimizer  FTRL、AdaGrad、AdaDelta、ADAM、AmsGrad、etc • Loss Function  LogLoss、SquareLoss、Cross Entropy、etc • 评估指标  AUC、Loss、MAE、RMSE  支持外部eval工具，计算MAP、NDCG MLX的模型能力 • 提供离线、近线、在线全流程解决方案，各阶段提供扩展方案，降低算法迭代成本； Random Forest 2. XGBoost 1. MLP 2. 少量特征空间 的Wide & Deep 1. 大规模离散特征 的Wide & Deep 2. DeepFM 3. Deep Cross 树模型 小规模DNN 大规模离散DNN • 超大规模深度学习  工程实现  数据并行、模型并行  在线、近线、离线逻辑一致性  实时模型  业务应用  召回模型，ANN搜索

Scikit-learn主要用法 交叉验证及超参数调优 from sklearn.model_selection import cross_val_score clf = DecisionTreeClassifier(max_depth=5) scores = cross_val_score(clf, X_train, y_train, cv=5, scoring=’f1_weighted’)

model that fit the data we have very well, but do poorly on new data (poor generalization ability). Cross-validation, regularization, Reducing dimensionality is another possibility. It is apparent that model with degree=3 seems good. We might be able to choose a good value for M using the method of “cross validation”, which looks for the value that does best at prediction one part of the data from the

case, classes are 0, 1, 2 and so on until 9) inputs. We use the sparse variant of the categorical cross entropy loss function so that we can use the index of the correct class for each example. The regular are ready to make certain trade-offs. We hope that this chapter helps more deep learning models to cross the finish line. The next chapter will introduce learning techniques to improve quality metrics like

computations), OBD exclusively relies on second derivatives with respect to each weight ( ) and ignores cross interaction between the weights . The authors demonstrated that pruning by taking the second-derivative the minimum and maximum values it observes for . Each quantization bin boundary is denoted by a cross. This is not ideal because the precision allocated to the range [-4.0, -2.0] or [2.0, 4.0] (spanning