disadvantages of each representation? 27 Vasiliki Kalavri | Boston University 2020 Reconstitution functions Insert (append-only): The reconstitution function ins starts with an empty bag and then inserts single pass over streaming tuples in their arrival order • Small space: memory footprint poly-logarithmic in the stream size • Low time: fast update and query times • Delete-proof: synopses can handle

configuration inside keras.json file. We can perform some pre-defined operations to know backend functions. 3. Keras ― Backend Configuration Keras 10 Theano Theano is an open source sub-classing Keras models. Core Modules Keras also provides a lot of built-in neural network related functions to properly create the Keras model and Keras layers. Some of the function are as follows:  many activation function like softmax, relu, etc.,  Loss module - Loss module provides loss functions like mean_squared_error, mean_absolute_error, poisson, etc.,  Optimizer module - Optimizer

Function) L ?, ? ? = ? − ? ? 2 3. 绝对损失函数(Absolute Loss Function) L ?, ? ? = ? − ? ? 4. 对数损失函数(Logarithmic Loss Function) L ?, ? ? ? = −log? ? ? 机器学习的概念-损失函数 23 根据上述损失函数模型，我们可知，损失函数值越小，模型性能越好。给定一个数据集，我们将

mean_absolute_percentage_error . . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.4 mean_squared_logarithmic_error . . . . . . . . . . . . . . . . . . . . . . . 134 7.2.5 squared_hinge . . . . . . . . . age_error mean_absolute_percentage_error(y_true, y_pred) 7.2.4 mean_squared_logarithmic_error mean_squared_logarithmic_error(y_true, y_pred) 7.2.5 squared_hinge squared_hinge(y_true, y_pred) 7.2

. . . . . . . . . . . . . . . . . . . . . . 209 10 Computational tools 211 10.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 10.2 . 215 10.3 Expanding window moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 10.4 Exponentially weighted moment functions . . . . . . . . . . . . . . . . . . objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 22.3 Calling R functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 432 22.4

in the input • k independent and uniformly distributed hash functions, where k << n The Bloom filter n bits h1 h2 hk … k hash functions ??? Vasiliki Kalavri | Boston University 2020 25 for i=1 to filter Adding an element to the filter 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 n bits h1 h2 hk … k hash functions stream elements x The empty filter is initialized to all 0s ??? Vasiliki Kalavri | Boston Testing if an element is in the filter 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 1 n bits h1 h2 hk … k hash functions test element x If all bits are set, the element may exist in the set. If at least one element

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 1.8.1.1 Window functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 111 1.8.1.2 Changes to rename files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 1.10.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 151 1.10.1.8 Changes to Excel with . . . . . . . . . . . . . . . . . . . . . . 477 8.2.13 DataFrame interoperability with NumPy functions . . . . . . . . . . . . . . . . . . . . . . 477 8.2.14 Console display . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . 92 8 Computational tools 95 8.1 Statistical functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 8.2 Moving . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 8.3 Exponentially weighted moment functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 8.4 Linear and panel regression Python . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 18.2 Calling R functions with pandas objects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 18.3

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 1.7.1.1 Window functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 109 1.7.1.2 Changes to rename XPORT files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.9.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 149 1.9.1.8 Changes to Excel with . . . . . . . . . . . . . . . . . . . . . . 475 8.2.13 DataFrame interoperability with NumPy functions . . . . . . . . . . . . . . . . . . . . . . 475 8.2.14 Console display . . . . . . . . . . . .

Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 1.2.2.12 Consistency of Range Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 1.2.2.13 No Automatic Matplotlib Converters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 1.10.1.1 Window functions are now methods . . . . . . . . . . . . . . . . . . . . . . . . . 140 1.10.1.2 Changes to rename files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 1.12.1.7 Support for Math Functions in .eval() . . . . . . . . . . . . . . . . . . . . . . . . . 180 1.12.1.8 Changes to Excel with