Roughly speaking links included here go with different major topics of the course.

- Lecture 3 Examples. This zip files contains five early tutorials as well as ex00movie that illustrates how to advance through frames of an mpeg video.

- Tomáš Bořil Fourier Series 3D. This site lets you control the construction of different functions by manipulating the phase and magnitude of constiuent parts of the signal. The visualization takes advanatage of a 3D view that is clever and allows more information be shown in a single presentation.
- Dave Watts Ejectamenta Fourier Site. Dave Watts has built an excellent 2D fourier transform visualization tool that allows one to move backward and forward between the spatial domain, e.i. a greyscale image, and the Frequency domain. For testing I prefer the following image of the CSU Oval. Pay attention to the Short Instructions and in particular the guideance on how to construct a low pass and a high pass filter. Also, if you want to test your skill, supress the 'noise' consisting of a sinusoidal disturbance in this image of the letter B.

The web contains many helpful tutorials on tensorflow. I have only begun to scratch the surface. That caveat offered, I found these helpful.

- TensorFlow 101 (Really Awesome Intro Into TensorFlow). This is a relatively long but I thought really excellent walk through to set the context for Tensorflow.
- TensorFlow Tutorial - How to use TensorFlow to Build a Neural Network. This is relatively shorter - just under 8 minutes - introduction with an emphasis on image recognition.
- Dan Aloni's blog post on Back Propagation with TensorFlow. We will use this tutorial to begin digging into the basics of using Tensorflow. This local file aloni_backprop.py is a copy of the tutorial code.

The Layers modul elevates the level of abstraction for constructing and running graphs in general and convolutional neural networks in particular. The examples and relateed material here represent a significant focus for our work on CNNs.

- tf.layers Module API description. Here we find the definitive defintion of the Layers API. Useful for details, but not a good substitute for the tutorial - see below.
- Layers MNIST Tutorial. There is a a lot going on in this example, and it clearly is constructed to help educate us on how to best take advantage of the Layers Module.
- There is source code on GitHub and for convenience here is a local copy.

Tensorboard is a powerful GUI tool that facilitates detailed summaries and hence analysis of models. Here are links to a tutorial using the MNIST dataset to demonstrate the use of summaires in tensorflow to support analysis of outcomes in tensorboard.

- TensorBoard: Visualizing Learning is an excellent starting point to learn more about how to connect a tensorflow model with tensorboard.
- There is also a Corresponding YouTube Presentation that walk through the demonstration code.
- The source code may be found on GitHub and for convenicence here is a local copy.

Even though this tutorial and the previous one illustrating Layers and Estimators solve the same problem of recognizing MNIST hand drawn digits, the structures of the models are different. Notably, the model illustrating summaries and tensorboard does not use any convolutional layers. Also, this example does not use Tensorflow Estimators.