This is the repository for the LinkedIn Learning course Training Neural Networks in Python. The full course is available from LinkedIn Learning.
Having a variety of great tools at your disposal isn’t helpful if you don’t know which one you really need, what each tool is useful for, and how they all work. In this course, take a deep dive into the innerworkings of neural networks, so that you're able to work more effectively with machine learning tools. Instructor Eduardo Corpeño helps you learn by example by providing a series of exercises in Python to help you to grasp what’s going on inside. Discover how to relate parts of a biological neuron to Python elements, which allows you to make a model of the brain. Then, learn how to build and train a network, as well as create a neural network that recognizes numbers coming from a seven-segment display. Even though you'll probably work with neural networks from a software suite rather than by writing your own code, the knowledge you’ll acquire in this course can help you choose the right neural network architecture and training method for each problem you face.
This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL to go to the branch you want to access.
The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter.
Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The master branch holds the final state of the code when in the course.
- To use these exercise files, you must have the following installed:
- Python 3
- NumPy
- Tkinter
- Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
- You may either edit the code in your favorite text editor and run from the command line, or you may use your favorite Python IDE. In the course videos you'll see the exercise files in Visual Studio Code.