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Introduction to Caffe&Tensor Flow&MxNet
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Duration：1 hours and 54 minutes and 8 seconds
Date：2018/04/06

Uploader：老白菜

Introduction

keywords： Neural Networks Machine Learning Bayesian clustering regression analysis convolution

(01) Preliminary machine learning and related mathematics
(02) Mathematical statistics and parameter estimation
(03) Matrix analysis and application (
04) Preliminary convex optimization
(05) Regression analysis and engineering application
(06) Feature engineering
(07) Workflow and model Tuning
(08) Maximum entropy model and EM algorithm
(09) Recommended system and application
(10) Clustering algorithm and application
(11) Decision tree random forest and adaboost
(12) SVM
(13) Bayesian method
(14) Topics Model
(15) Bayesian inference sampling and variation
(16) Artificial neural network
(17) Convolutional neural network
(18) Recurrent neural network and LSTM
(19) Caffe&Tensor Flow&MxNet Introduction
(20) Bayesian network and HMM
(extra Supplement) word embedding word embedding

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