use coursera-dl coursera-dl -u username -p password –cauth fromcookie -f “pptx ppt pdf” –specialization deep-learning coursera-dl -u username -p password –cauth fromcookie -f “pptx ppt pdf” machine-learning https://github.com/coursera-dl/coursera-dl https://github.com/coursera-dl/coursera-dl/issues/773
Monthly Archives: January 2021
split and merge tar.gz file
split: tar -czf – ~/work | split –bytes=100MB – ~/workspaces.tar.gz https://learner.coursera.help/hc/en-us/articles/360004990332-Download-Jupyter-Workspace-files merge cat *.tar.gz* | tar xvfz – https://stackoverflow.com/questions/38199571/untar-multiple-tar-gz-aa-tar-gz-ab-pattern-files
coursera deep learning course data
there are large files (>100M), which can not be downloaded from coursera directly. found they’re available here: https://github.com/kotestyle/deep-learning-coursera
Word2Vec skip-gram model
Besides Andrew Ng’s lecture, a good discussion can be found here: https://towardsdatascience.com/word2vec-skip-gram-model-part-1-intuition-78614e4d6e0b https://towardsdatascience.com/word2vec-skip-gram-model-part-2-implementation-in-tf-7efdf6f58a27
resize image to a specific resolution/pixel
convert -resize 1024X768 source.png dest.jpg #(keep aspect ratio) convert -resize 1024X768! source.png dest.jpg #(not keep aspect ratio) https://askubuntu.com/questions/1164/how-to-easily-resize-images-via-command-line just a side note on how to resize image in scipy for cnn. my_image = “image.jpg” # change this to the name of your image filefname = “images/” + my_imageimage = np.array(ndimage.imread(fname, flatten=False))my_image = scipy.misc.imresize(image, size=(300,225)).reshape((3002253,1))my_image = …
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download jupyter files from coursera
Execute !tar cvfz allfiles.tar.gz * in a cell https://www.reddit.com/r/learnmachinelearning/comments/7er5ps/coursera_downloading_all_the_assignments_jupyter