Join us and get access to hundreds of tutorials and a community of expert Pythonistas.

Unlock This Lesson

This lesson is for members only. Join us and get access to hundreds of tutorials and a community of expert Pythonistas.

Unlock This Lesson

Hint: You can adjust the default video playback speed in your account settings.
Hint: You can set the default subtitles language in your account settings.
Sorry! Looks like there’s an issue with video playback 🙁 This might be due to a temporary outage or because of a configuration issue with your browser. Please see our video player troubleshooting guide to resolve the issue.

Building a Neural Network & Making Predictions (Summary)

Congratulations! You built a neural network from scratch using NumPy. With this knowledge, you’re ready to dive deeper into the world of artificial intelligence in Python.

In this course, you learned:

  • What deep learning is and what differentiates it from machine learning
  • How to represent vectors with NumPy
  • What activation functions are and why they’re used inside a neural network
  • What the backpropagation algorithm is and how it works
  • How to train a neural network and make predictions

The process of training a neural network mainly consists of applying operations to vectors. Today, you did it from scratch using only NumPy as a dependency. This isn’t recommended in a production setting because the whole process can be unproductive and error-prone. That’s one of the reasons why deep learning frameworks like Keras, PyTorch, and TensorFlow are so popular.

For additional information on topics covered in this course, check out these resources:

Download

Sample Code (.zip)

23.0 KB

Download

Course Slides (.pdf)

642.7 KB

Become a Member to join the conversation.