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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:

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Course Slides (.pdf)

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Santosh on Dec. 27, 2021

Fantastic. Brilliantly explained.

Rafael on Sept. 20, 2022

Good explanation, but why, why there is no final example, where one vector would be given to the trained neuronal net with explanation:

-This Vector was given because…

-We await the prediction of…

-The trained model predicted x, because…

Short, to run the model and interpret the results by random input.

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