Swift and Lewis have been working closely with Few & Far Between throughout the entire process, digging out the treasure trove of quarter-century old DATs that were knocking around in their respective homes. Remastered and pressed from the original DATs dug out by Language members Alex Swift and Mark Lewis, In The Lab receives a crisp new lease of life with a fresh pressing. Founder of Few & Far Between, Sam Hall, makes the 1992 deep house record the figurehead of his new label, setting the scene for the forthcoming original releases and further reissues in 2018 and beyond. Hope this helps add to Serzan's excellent answer above.26 years after its initial release, In The Lab EP by Language receives a reissue via Few & Far Between, a new imprint out of Bristol. It will pull all the necessary parts together as long as they are in the same folder. This is the file named .part.aa for the example used. Just select the first file in the series for extraction with 7-Zip. The final task is to untar the multi-part archive. Each part will have an extension like .part.xx. This will split the archive into multiple parts each of size 50 Mb (or your preferred size setting). So add one more line of code to split files into manageable chunk sizes as follows: !split -b 50m .part. The resulting tarball is too large to download via browser. However, this course has individual files of size 100's of MB and folders with 100's of sample images. This produces a tarball which, if small enough, can be downloaded from the Jupyter notebook itself and unzipped using 7-Zip. Start with the following line of code as suggested in the post by Serzan Akhmetov above: !tar cvfz * Here's what I used to successfully download all assignments with the associated files and folders to my local Windows 10 PC. Along with the notebooks are folders with large files.

The curriculum uses Jupyter Notebooks online. Andrew Ng's Deeplearning.ai program via Coursera.

Download in the lab