Pinned Repositories
flask
The Python micro framework for building web applications.
Gender-Recognition-by-Voice-0.97004-Accuracy-
The voices of different people are tested for 20 properties. These properties include mean-frequency, standard deviation, kurtosis, skew, mode frequency , modulation index , fundamental freq....,etc . My work includes the demonstation of the much probable properties showcased by females as well as males. The study of important attributes for voice recognition and their varied concentration in each gender using the inferences drawn from the various regression plots, pair plots, scatter plots , etc. Dataset is also standardized or normalized prior to training for better performance. Different models are tried . Also plotted their accuracy curves to understand the variation of parameters wrt accuracy. The parameters were tuned using repetitive piecewise gridsearch to compute things efficiently wrt time. Support Vector Machines are taken much care off till end and gave a cross-validated accuracy of 97.004 %. Further a train test spilt accuracy of 99.36 % given by XGBoost Classifier.
machine_learning_beginner
机器学习初学者公众号作品
recommenders
Best Practices on Recommendation Systems
test717
test717-1
test
Unsupervised-Segmentation
A high performance impermentation of Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki
jxgao96's Repositories
jxgao96/flask
The Python micro framework for building web applications.
jxgao96/Gender-Recognition-by-Voice-0.97004-Accuracy-
The voices of different people are tested for 20 properties. These properties include mean-frequency, standard deviation, kurtosis, skew, mode frequency , modulation index , fundamental freq....,etc . My work includes the demonstation of the much probable properties showcased by females as well as males. The study of important attributes for voice recognition and their varied concentration in each gender using the inferences drawn from the various regression plots, pair plots, scatter plots , etc. Dataset is also standardized or normalized prior to training for better performance. Different models are tried . Also plotted their accuracy curves to understand the variation of parameters wrt accuracy. The parameters were tuned using repetitive piecewise gridsearch to compute things efficiently wrt time. Support Vector Machines are taken much care off till end and gave a cross-validated accuracy of 97.004 %. Further a train test spilt accuracy of 99.36 % given by XGBoost Classifier.
jxgao96/machine_learning_beginner
机器学习初学者公众号作品
jxgao96/recommenders
Best Practices on Recommendation Systems
jxgao96/test717
jxgao96/test717-1
test
jxgao96/Unsupervised-Segmentation
A high performance impermentation of Unsupervised Image Segmentation by Backpropagation - Asako Kanezaki