机器学习基础知识介绍
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1.Machine Learning Tutorial Basic introduction
2. Computer Version Nature Language Processing Robotic/Automatic Biology
3.
4.Basic Concept Cross Validation Over Fitting Activation Function Supervised Learning UnSupervised Learning Batch Epoch Layer Train Set Test Set Feature Dimensionality Generalization Maxpooling L1 /L2 Benchmark Baseline
5.Basic Concept
6.Basic Concept
7.Supervised/Unsupervised Learning
8.Supervised Learning
9.Supervised Learning
10.Unsupervised Learning
11.Basic Algorithm K NN K Means Naïve Bayes Decision Tree Gradient Boosting algorithms SVM Random Forest Linear Regression Logistic Regression Dimensionality Reduction Algorithms
12.How do you choose a machine learning algorithm ?
13.How do you choose a machine learning algorithm ?
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15.Output Input Feature Engineering Data PreProcessing
16.With Python Or R Python and Pythonic ML library Common library
17.With Python Or R Python and Pythonic ML library Common library Don’t fighting language, Pythonic
18.With Python Or R Python and Pythonic ML library Common library Tensorflow Keras Scikit -Learn Pytorch
19.With Python Or R Python and Pythonic ML library Common library Pandas Numpy Scipy Matplotlib Pyplot Ndarray Scikit -image Image
20.R: I forget it ... Easily And Powerful
21.Examples: DGA/XSS/ Webshell MNIST
22.MNIST DGA/XSS/ Webshell
23.Mnist And Fashion- Mnist Pro Beginners Online Demo
24.Algorithm 1 : KNN
25.Principle Wikipedia
26.Principle
27.Code in Python
28.Code in Python
29.Algorithm 2 : SVM