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  • beijing ,china
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View pinyin_content_v1.md

conclusion

Learning pinyin is standard among native-speaking and non-native-speaking learners alike. That alone says something about how useful it can be!

Chinese is a tonal language. That means how you say things determines the meaning of what you’re saying. The importance of tones cannot be understated. For someone new to the language it can be incredibly difficult to detect tonality in someone’s speech. It can be so hard that new learners believe the differences must make little difference to meaning and put their attention elsewhere. Believe me, the differences are not minor, and you MUST pay attention to tones. That’s why you should learn pinyin.

tones lag

View gist:d0b71755b5fb33e388d8d23663eb15e8
When people can demonstrate their potential, the possibilities are endless.
View install_opencv4.01_from_source.sh
cmake -D OPENCV_EXTRA_MODULES_PATH=../../opencv_contrib-4.0.1/modules \
-D CMAKE_BUILD_TYPE=RELEASE \
-D CMAKE_INSTALL_PREFIX=/usr/local \
-D INSTALL_PYTHON_EXAMPLES=ON \
-D INSTALL_C_EXAMPLES=OFF \
-D PYTHON_EXECUTABLE=$(which python2) \
-D PYTHON3_EXECUTABLE=$(which python3) \
View tracking_dataset_download_link.md
@gjpicker
gjpicker / pytorch_elf_install.md
Created Nov 30, 2018
pytorch ELF install in GCC 4.8
View pytorch_elf_install.md

#step1 install gcc8.1 follow this site install_gcc8.1 #step 2 follow ELF offical install instruction ,stop before "make"

#step 3 export some env info

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gjpicker / imgs_caricature_urllist.json
Last active Nov 8, 2018
crawler_img_list_from_pinterest_com
View imgs_caricature_urllist.json
["https://i.pinimg.com/736x/97/da/0e/97da0ed3f6935c6ac48bde9568655888.jpg", "https://i.pinimg.com/736x/0b/5a/47/0b5a4721acd89550bfde5dc45eee0470.jpg", "https://i.pinimg.com/736x/52/53/29/525329ea21b89d4d1a5e8847a15183c2.jpg", "https://i.pinimg.com/736x/10/ad/c2/10adc2b3807f3208d3e06414ab89cf2e.jpg", "https://i.pinimg.com/736x/57/ea/88/57ea88bfdafd552103547de183e5fcdc.jpg", "https://i.pinimg.com/736x/2b/1e/ba/2b1eba2b915da6437ea728a233d613d2.jpg", "https://i.pinimg.com/736x/d0/a3/39/d0a3390ac67b1912ea0b7bbe8da3849d.jpg", "https://i.pinimg.com/736x/ef/a7/08/efa708ce603503857d1bcac3e631cc59.jpg", "https://i.pinimg.com/736x/e5/40/85/e540856ea7f25c1c23efaf95402f2fcc.jpg", "https://i.pinimg.com/736x/58/b8/6c/58b86c64fc59ceb179a7ea21873be8c0.jpg", "https://i.pinimg.com/736x/4e/c4/a3/4ec4a32fb37cebeb81cd4727e6d38fec.jpg", "https://i.pinimg.com/736x/15/2d/c6/152dc623bfabb84530e6e4daa91cf646.jpg", "https://i.pinimg.com/736x/9f/9b/1b/9f9b1bac7f82aefe89234328f6a67428.jpg", "https://i.pinimg.com/736x/07/80/35/07803560114c
View gist:6bf0b010784add8ed3d24f851075ba0c
http://www.gatsby.ucl.ac.uk/~balaji/Understanding-GANs.pdf
compare GAN with VAE and Classic Probabilistic ML
some application practise of GAN ,like Wasserstein GAN
some research directions