From the course: Building Deep Learning Applications with Keras

How to use Codespaces and the exercise files

From the course: Building Deep Learning Applications with Keras

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How to use Codespaces and the exercise files

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- [Instructor] Here is some great news. We are leveraging GitHub Code Spaces in this course. It provides a cloud-powered development environment. You can engage with these environments directly from Visual Studio Code or through a browser-based editor. Coding has never been this flexible and accessible. Let's explore how you can harness this tool to enhance your learning experience. Firstly, access the repository, sign into your GitHub account, and go to the course repository containing the materials. Next, locate the code tab on the repository page, typically, near the add file tab on the upper-right. Click code, find code spaces, and then click the plus sign to create a new code space. After the setup, a browser-based editor will launch, providing a fully functional development environment. If you already have a code space you have created previously, it'll show up down below. Click on the three dots, and then click on the open in browser option. In the same menu, you can also choose to open it in Visual Studio Code. This will open in the desktop version. Once the Code Space's web version is ready, it'll look something like this. You can also open it in Visual Studio Code desktop by clicking the code spaces on the lower-left corner on the web version and then selecting open in Visual Studio Code desktop. Now, let's look at the folder structure. Within the SRC folder, exercise files are neatly organized by chapter. Each coding lesson starts with a Begin.py and ends with an End.py. You will run your code using the built-in terminal, just as you would locally in Visual Studio Code. For mobile participants, if you are on a mobile device or lack access to the exercise files, you can still follow along and absorb the material during the walkthroughs. In addition to the SRC folder we went over, we also have input folder. This is meant for any input files needed by the code. We also have data folder inside the input folder. Data folder contains data sets like Tesla_NASDAQ_Prediction.csv, which will be used for model training. We then have the image folder which stores images related to the project. We also have the output folder for the artifacts produced by your code. Now, let's get coding. Start with the Begin.py files and progress through the lessons, transforming your code into the End.py files. The transition from the initial to the final state of each script will mirror your growth in understanding Keras and deep learning principles. GitHub Code Spaces is a leap towards modern, convenient, and collaborative coding education. It allows you to focus solely on learning and application, eliminating the setup barriers. Ready to start? Let's code, create, and launch into the cloud.

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