Test

.devcontainer/Dockerfile

@@ -12,6 +12,7 @@ RUN if [ "${NODE_VERSION}" != "none" ]; then su vscode -c "umask 0002 && . /usr/
 # COPY requirements.txt /tmp/pip-tmp/
 # RUN pip3 --disable-pip-version-check --no-cache-dir install -r /tmp/pip-tmp/requirements.txt \
 #    && rm -rf /tmp/pip-tmp
+RUN pip3 install numpy
 
 # [Optional] Uncomment this section to install additional OS packages.
 # RUN apt-get update && export DEBIAN_FRONTEND=noninteractive \

.vscode/settings.json

@@ -20,5 +20,6 @@
   "workbench.activityBar.visible": true,
   "workbench.colorTheme": "Visual Studio Dark",
   "workbench.fontAliasing": "antialiased",
-  "workbench.statusBar.visible": true
+  "workbench.statusBar.visible": true,
+  "python.linting.pylintCategorySeverity.convention": "Hint"
 }

NOTICE

@@ -4,9 +4,6 @@ All Rights Reserved.
 Licensed under the LinkedIn Learning Exercise File License (the "License").
 See LICENSE in the project root for license information.
 
-ATTRIBUTIONS:
-[PLEASE PROVIDE ATTRIBUTIONS OR DELETE THIS AND THE ABOVE LINE “ATTRIBUTIONS”]
-
 Please note, this project may automatically load third party code from external 
 repositories (for example, NPM modules, Composer packages, or other dependencies). 
 If so, such third party code may be subject to other license terms than as set 

README.md

@@ -1,7 +1,12 @@
-# Training Neural Networks in Python
-This is the repository for the LinkedIn Learning course `Training Neural Networks in Python`. The full course is available from [LinkedIn Learning][lil-course-url].
+# Training Neural Networks in Python 
+This is the repository for the LinkedIn Learning course Training Neural Networks in Python. The full course is available from [LinkedIn Learning][lil-course-url].
+
+![Training Neural Networks in Python ][lil-thumbnail-url]
+
+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 learn the inner workings 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.
+<br><br>
+This course is integrated with GitHub Codespaces, an instant cloud developer environment that offers all the functionality of your favorite IDE without the need for any local machine setup. With GitHub Codespaces, you can get hands-on practice from any machine, at any time—all while using a tool that you’ll likely encounter in the workplace. Check out the [Using GitHub Codespaces with this course][gcs-video-url] video to learn how to get started.
 
-_See the readme file in the main branch for updated instructions and information._
 ## Instructions
 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.
 
@@ -19,16 +24,12 @@ To resolve this issue:
 
     Add changes to git using this command: git add .
 	Commit changes using this command: git commit -m "some message"
+### Instructor
 
-## Installing
-1. To use these exercise files, you must have the following installed:
-	- [list of requirements for course]
-2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.
-3. [Course-specific instructions]
-
-
-[0]: # (Replace these placeholder URLs with actual course URLs)
+Eduardo Corpeno
 
-[lil-course-url]: https://www.linkedin.com/learning/
-[lil-thumbnail-url]: http://
+Check out my other courses on [LinkedIn Learning](https://www.linkedin.com/learning/instructors/eduardo-corpeno?u=104).
 
+[lil-course-url]: https://www.linkedin.com/learning/training-neural-networks-in-python-17058600
+[lil-thumbnail-url]: https://cdn.lynda.com/course/3215347/3215347-1667864479246-16x9.jpg
+[gcs-video-url]: https://www.linkedin.com/learning/training-neural-networks-in-python-17058600/using-github-codespaces-with-this-course

src/MLP.py

@@ -0,0 +1,16 @@
+import numpy as np
+
+class Perceptron:
+    """A single neuron with the sigmoid activation function.
+       Attributes:
+          inputs: The number of inputs in the perceptron, not counting the bias.
+          bias:   The bias term. By default it's 1.0."""
+
+    def __init__(self, inputs, bias = 1.0):
+        """Return a new Perceptron object with the specified number of inputs (+1 for the bias).""" 
+        pass
+
+    def run(self, x):
+        """Run the perceptron. x is a python list with the input values."""
+        pass
+