adding projects to repo

README.md

@@ -1,3 +1,69 @@
 # dtomlinson-cv
+Repo showcasing example work
 
-Repo showcasing example work
+## Image Classifier using a Nerual network
+
+This is an example of the Udacity project `Machine Learning - Introduction Nanodegree Program` I completed showing the steps taken to train an image classifier neural network on images of flowers.
+
+The data set is too large for git but examples of the images are available in the jupter workbook. We create a neural network to identify 102 different categories of flowers.
+
+We then create utility functions `train.py` and `predict.py` to update the model and predict a flower from the command line, without having to retrain the whole model.
+
+### `train.py`
+
+You can select either Alexnet or VGG13 as a base model
+
+Arguments:
+
+1. `data_dir: str` - Specify path to data directory
+2. `save_dir: str` - Specify path to output directory
+3. `arch: str` - Choose the base model
+4. `lrn: float` - Specify the learning rate
+5. `hidden_units: int` - Specify the hidden layers
+6. `epochs: int` - Specify the epochs
+7. `GPU: str` - If a GPU is available set this to true
+
+### `predict.py`
+
+Arguments:
+
+1. `image_dir: str` - Specify path to image
+2. `load_dir: str` - Specify path to load in
+3. `top_k: str` - Specify top k classes that are most likely
+4. `category_names: str` -- Specify JSON of mapping of categories to real names
+5. `GPU: str` - If a GPU is available set this to true
+
+
+## Anomaly Detection using a Neural network
+
+Included is a jupyter notebook outlining the general steps during the development of a Neural Network to identify anomalies in key business metrics.
+
+The general idea is
+
+1. Fit a distribution to the data, once we have this we can find the probability of a value being an anomaly. This turns our problem form unsupervised to supervised as we can say (with a certain probability) whether or not we think a value is an anomaly. This supervised set is passed on.
+
+2. Create a baseline Neural Network, experiment with the hyperparameters and Stochastic Gradient Descent and a Gradient Boosting classifier.
+
+## Vrops API
+
+A python wrapper for the VROPS api. No such wrapper exists publicly to automate pulling statistics from VMs using VROPS. See the `README.md` inside the project for detailed documentation.
+
+This will be released publically as a module available in pypi.org (`pip`)
+
+## Emoji finder
+
+A simple script (for fun) that can lookup an emoji on the command line by name, tag or description.
+
+## (Udacity) Supervised Learning Project
+
+Supervised learning project from the Udacity Machine Learning course. This showcases an example of fitting several supervised learning models to a given dataset where the aim is to classify people on how likely they are to donate to a charity, based on their income, job, marital status etc. See the `finding_donors.ipynd` jupter workbook for a walktrhough through the project.
+
+## Splunk dashboard screenshotter
+
+For Splunk there is no easy way to automate screenshots of dashboards. You can set a report to send a single graph as a PDF, or you can use the API to pull your data and use matplotlib (for example) to graph and send your data via email.
+
+This script uses Selenium to log in, hide certain elements, and screenshot the body. It uses postfix to send an email directly from the server.
+
+The script is complete - I will write this as a custom splunk command `(| dashboardscreenshot $NAME $EMAIL )` and bundle it into a Splunk app to release publicly on SplunkBase.
+
+Included is an example PNG of a random website to showcase the functionality.