I am auditing for free some courses in Andrew Ng's various ML specialization on Coursera which gives me access to all video lectures, but sessions in which you can run code examples in a jupyter notebook are locked, so I was wondering if there's like a github repo that collects these notebooks where I can have a peek at some code snippets mentioned in the video lectures?
BTW, if anyone's not aware, some of Deeplearning.AI team's advanced specializations have github repos like this that offers you all codes for free and are organized by courses and weeks, which is quite neat, and great for people who only need a subset of topics in the whole specialization. Unfortunately there's no official repos for the beginner level courses which are the ones I'm currently working on. So I'm wondering if anyone had collected these notebooks and are willing to share with free users, provided that there's no copyright issues of course.
Coursera Download Jupyter Notebooks
Download Zip 🔥 https://urllie.com/2y3hXw 🔥
I am trying to submit an assignment in Coursera related to machine learning, but when I am unable to save the notebook in the embedded jupyter notebook and seeing forbidden, don't know what's wrong, I tried to raise the complaint to coursera support but haven't heard back from them, its been 3days and i don't want to loose the traction of it. please help me resolve the issue
As mentioned in the coursera help articles in order to download notebooks from the class we need to zip all the content of root folder into single file and download the final workspace.tar.gz using these steps: but it is not working all courses.
Anyone knows proper way to do this !!
Jupyter is a web application used to create and share documents. These documents include codes, images, and text. The open-source platform allows you to create different notebooks for your codes with your explanations, data, and results. With Jupyter, you are able to share your notebooks as an attachment to collaborate with others and improve your results.
To learn about Jupyter, you should have prior knowledge in coding and data analysis. Having this prior knowledge will allow you to understand how to create the notebooks and analyze the results. It will also help you create simulations and be able to learn from other's shared data. Before starting to learn about Jupyter you should also have knowledge in coding programs and languages to understand the language and be able to translate the results to test modifications or run other simulations.
Once you have finished the course, it would be interesting to still be able to read, run and experiment with the Notebooks from the course. After I passed the certification, I felt I had understood the material and knew when to apply it, but it felt I would need to go back from time to time, or just dig a bit deeper later on and play with it some more to really get it down. So running the notebooks locally, would save me a lot of trouble.
Coming from a more C#-world and having limited experience running these notebooks, I was looking for a way to do this. I initially tried downloading each file manually, which turned out not the be a good idea, so I started the browsing the Deep Learning Specialization forums and found a number of students having the same issues.
I have enrolled in some courses on Coursera that have assignments that need to be completed in a Jupyter notebook. Jupyter notebooks are awesome, but the Coursera-hub of those notebooks is very frustrating.
I am going through ML Specialization from Coursera and want to use DataLore to replace Coursera-embedded Jupyter notebooks. There is an image folder I can download from Coursera. I can upload it to the workspace (preferred) or notebook files in DataLore. So far, so good. But referencing these images in Markdown cells does nothing.
For each course I created a dedicated jupyter notebook for course notes, organized by week and weekly sub-topics (these notebooks are available on github here). I would pause throughout each lecture video to add concise notes to my notebook, including screen captures of the most important slides. I often had the experience of beginning to type up some notes, only to realize there was some gap in my understanding which prompted me to skip back in the video to clarify; without taking notes I wouldn't even have been aware of my own misunderstandings. Whenever it felt like we were moving into a new topic I would add a summary section to the notebook where I would read back through my notes and try to summarize the content as concisely as possible. I highly recommend this approach for these reasons:
The homeworks are in the form of jupyter notebooks served out from a remote server, which have a submission button for grading. At the end of each course I looked back through the homework notebooks and pulled out any functions and code snippets that I wanted to be able to reference quickly, and also typed up summaries of how the code was organized in the homeworks, as I found this very helpful for solidifying the ideas. Many of the homework notebooks are behind a paywall, so I also made sure to download the full jupyter workspace for the course (not just the homeworks, but data and helper scripts also) so I could rerun the homework locally after my subscription ended. Instructions for that are here.
Thanks for the useful advice.
I managed to download and unzip all files to my local PC following your instructions. However, when I upload one of the notebooks to jupyter notebook, the embedded images are not shown. Do you have any ideas what are the reasons?
You are currently looking at version 1.1 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the Jupyter Notebook FAQ course resource.
The specialization consists of three courses which you can take independently: Linear Algebra for Machine Learning and Data Science, Calculus for Machine Learning and Data Science, and Probability & Statistics for Machine Learning & Data Science. Each course consists of a number of bite-sized video lectures interspersed with some practice quizzes and programming exercises run in hosted Jupyter notebooks. The courses are expected to be completed over the course of some weeks, and each week has a final quiz and usually a programming lab. This format makes it perfect for casual learning, where you can sneak in a video in those seven minutes between meetings or so.
We will be using Instabase for this assignment. You will need the files found at the following link copied to your personal Instabase account: Assignment 3.
To copy files to your personal Instabase account, first select all of the files by pressing the "Shift" key while simultaneouslyclicking on each of the files. This will cause an "Actions" dropdown menu to appear above the file names. Click on this dropdownmenu, choose "Copy To", and then copy the files over to your own private Instabase folder (you can create a new private folder at this step if you want).Once you've done this, you should have a private copy of the assignment to work on. Navigate to the folder where you copied the files to, and the folder should contain PythonAssign.ipynb, Players.csv, Teams.csv, and Titanic.csv. Right-click on PythonAssign.ipynb and select Open With > Jupyter. (If you simply double-click on it, it will show you the file but will not run Jupyter notebooks.) Sometimes it will take a minute or so for a new Jupyter server to start up on your behalf. Once it does, you are ready to go! In the notebook you will see clearly where you need to add code for the different problems.
Hi, I developed a code autocompletion plugin for Jupyter Notebook based on TabNine which provides code auto-completion with deep learning. Equipped this will make you more productive, Welcome to use:) -wu/jupyter-tabnine
Drag and drop is probably the best new feature that is added to jupyter notebooks. The ability to re order cells without cut and paste is powerful. It also feels more natural to do drag and drop given that code is organized in cells in notebooks.
This is my personal favorite feature. Having my blog posts in markdown format, I used to export notebooks in markdown format and then use Atom to edit the exported file, then run a local server to see the output of my edits. With this new feature, I can edit and see in real time the update of my markdown files in jupyter Lab. This speeds-up the edit process and streamlines work.
This is another favorite feature. Having to sync my work with my Github, I usually have to open another terminal and then commit the changes that I have made. With multiple windows open at the same time, I can have multiple notebooks that I am working on and then use a terminal inside jupyter Lab and commit my work. Again, awesome for streamlining work.
Once you had installed Supervisor, you can proceed with creating a directory to keep your Jupyter Notebook documents. For example, you can run the following command to create the jupyter-notebook directory inside the home directory:
Once you had created the virtual environment with Jupyter Notebook installed, proceed to create a shell script to run Jupyter Notebook. To do so, run the following command to create a shell script at ~/run-jupyter-notebook.sh: ff782bc1db