Explore Deep Neural Networks with IBM

Explore Deep Neural Networks with IBM

Table of Contents

1. Introduction
2. Setting Up the IBM Data Science Workbench
3. Accessing the Jupiter Notebook
4. Managing Files and Directories
5. Running Python Code
6. Installing Packages
7. Checking Versions
8. Submissions and Assignments
9. Using the IBM Data Science Workbench as a Learning Tool
10. Conclusion

Article

Introduction

Welcome to the world of deep neural networks! In this article, we will explore the various applications and uses of deep neural networks.

Setting Up the IBM Data Science Workbench

If You're interested in submitting assignments and working through examples in this class, you will need a Python environment with Charism TensorFlow installed. One approach is to install Python, Keras, and TensorFlow on your local computer. However, this may not be suitable for everyone. An alternative option is to use the IBM Data Science Workbench, a free web-Based application provided by IBM.

Accessing the Jupiter Notebook

To access the IBM Data Science Workbench, you will need to sign up and log in. Once logged in, click on the Jupiter Notepad, which will launch a Jupiter notebook for you. Note that this may take a few minutes, especially the first time you use it. The workbench ensures you have a virtual machine with sufficient resources, making it easier for you to run tensorflow and Keras code.

Managing Files and Directories

Within the IBM Data Science Workbench, you have access to a data directory where you can upload and store your data files. By default, the scripts assume that your data is in a subdirectory called "data." Additionally, the workbench provides other directories and folders for organizing your files effectively.

Running Python Code

The Jupiter notebook within the IBM Data Science Workbench enables you to write and execute Python code. It functions in the cloud, and every run generates a response from IBM. This eliminates the need for local execution and allows for smoother collaboration and sharing of code.

Installing Packages

While IBM has already installed multiple packages for you, there might be instances where you need to install additional packages. Fortunately, this can be done within the workbench using the command line. To install a package, simply prefix the command with an exclamation point (!). For example, "pip install scikit-learn" can be run as "!pip install scikit-learn."

Checking Versions

To ensure compatibility with the examples in this class, it is essential to check the versions of installed packages. By running a provided code block, you can verify that all packages are correctly installed. If any issues arise, IBM Data Science Workbench allows you to troubleshoot and resolve them efficiently.

Submissions and Assignments

The IBM Data Science Workbench provides a convenient feature for submitting assignments. By following the instructions outlined in a dedicated video, you can easily submit your work through the workbench. It also provides sample paths for referencing files located within the workbench.

Using the IBM Data Science Workbench as a Learning Tool

The IBM Data Science Workbench is a powerful tool that can enhance your learning experience. Whether you prefer to run everything from the cloud or simply want to explore an alternative approach, the workbench offers a range of features and benefits. However, it is important to note that this is just one option, and installing Python and Relevant packages on your local computer is also a viable choice.

Conclusion

In conclusion, the IBM Data Science Workbench is a valuable resource for working with deep neural networks. It provides a cloud-based environment where you can write, execute, and collaborate on Python code. Whether you choose to utilize the workbench or set up a local environment, the possibilities for exploring the applications of deep neural networks are vast. So dive in and start harnessing the power of deep neural networks today!

Highlights

• Explore the applications of deep neural networks
• Set up the IBM Data Science Workbench for seamless execution
• Access the Jupiter notebook within the workbench
• Manage files and directories effectively
• Run Python code in the cloud with IBM's resources
• Install packages as needed for your projects
• Verify Package versions for compatibility
• Submit assignments and work on projects
• Utilize the workbench as a learning tool
• Choose between a cloud-based or local environment

FAQ

Q: Can I use the IBM Data Science Workbench instead of installing Python on my local computer? A: Yes, the IBM Data Science Workbench can be a convenient alternative if you don't want to or can't install Python locally.

Q: How do I submit assignments using the IBM Data Science Workbench? A: There is a dedicated video tutorial that walks you through the process of submitting assignments through the workbench.

Q: Can I access and manage my files within the workbench? A: Yes, the workbench provides a data directory where you can upload and organize your data files.

Q: Are all the necessary packages already installed in the IBM Data Science Workbench? A: IBM has pre-installed multiple packages, but you may need to install additional packages as needed.

Q: Is the IBM Data Science Workbench free to use? A: Yes, the IBM Data Science Workbench is a free web-based application provided by IBM.

Q: Can I use the IBM Data Science Workbench as a learning tool? A: Absolutely! The workbench offers a range of features that can enhance your learning experience with deep neural networks.