Deep dive into machine learning

Neural NetworksMachine LearningBayesianclustering

(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) Topic 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

Total of 21 lessons1 days and 22 hours and 12 minutes and 36 seconds