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

### Early OpenCV Examples

• 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.

### Interactive Fourier Transform Sites

• 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.

### Tensorflow

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

### Tensorflow, Layers and Estimators

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.

### Tensorflow and Tensorboard

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.

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.