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:
- Look Ma, No For-Loops: Array Programming With NumPy
- Linear Regression in Python
- Practical Text Classification With Python and Keras
- Pure Python vs NumPy vs TensorFlow Performance Comparison
- PyTorch vs TensorFlow for Your Python Deep Learning Project
Congratulations, you made it to the end of the course! What’s your #1 takeaway or favorite thing you learned? How are you going to put your newfound skills to use? Leave a comment in the discussion section and let us know.