If you're willing to understand how neural networks work behind the scene and debug the back-propagation algorithm step by step by yourself, these slides should be a good starting point.
We will cover deep learning popular applications, the concept of the artificial neuron and how it relates to the biological one, the perceptron and the multi-layer one. We'll also dive in activation functions, loss functions and formalize the training of a neural net via the back-propagation algorithm.
In the last part, you'll learn how to code a fully functioning trainable neural network from scratch. In pure python code only, with no frameworks involved.
All the code is available on my github repo.
You can view the slides here:
Here's an animated slideshow of the back-propagation: