This tutorial covers the basics of Deep Learning with Convolutional Neural Nets. The tutorial is broken into two notebooks. The topics covered in each notebook are:

• Linear Regression as single layer, single neuron model to motivate the introduction of Neural Networks as Universal Approximators that are modeled as collections of neurons.

• Loss/Error functions, Gradient Decent, Backpropagation, etc

• Neural Network with hands on Tensorflow Implementation

• Convolutions and examples of simple image filters to motivate the construction of Convolutionlal Neural Networks.
• Example: CNN on Mnist
  • Visualizing Data
  • Constructing simple Convolutional Neural Networks
  • Training and Inference
  • Visualizing/Interpreting trained Neural Nets
• Example: CNN on CIFAR-10

The code examples presented here are mostly taken (verbatim) or inspired from the following sources. I made this curation to give a quick exposure to very basic but essential ideas/practices in deep learning to get you started fairly quickly, but I recommend going to some or all of the actual sources for an in depth survey:

• CS231n: Convolutional Neural Networks for Visual Recognition
• Deep Learnig Specialization, Andrew Ng
• Deep Learning with Python
• Keras Blog
• HAL Training Series
• ALCF Training Series