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README.md

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 [![Udacity - Self-Driving Car NanoDegree](https://s3.amazonaws.com/udacity-sdc/github/shield-carnd.svg)](http://www.udacity.com/drive)
 
-### Have the model parameters been tuned appropriately?
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-Used `adam` optimizer for ease of tuning other parameters and the quickness of prototyping newer and more effcient models.
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-### Is the training data chosen appropriately?
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-Choosing the training data was certainly the most important aspect of this project. My training data consisted of the following:
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-* 2 optimal laps around the track maintaining centrality in the lane as much as possible
-* Small clips starting at the edges of the track and moving to the middle. Emphasis was placed on the edges of curves as those were particularly problematic during inital tests.
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-### Architecture and Training Documentation
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-When picking a solution I began with the model introduced in the `Even More Powerful Network`. As I began to have trouble with the validation accuracy and testing using `drive.py` I introduced more training data for tricky areas, particular edges for curves. After I determined my training data to be sufficient, I found the model to be overfitting around epoch 3. In response, I added dropout to the bottom 2 fully connected layers and found the model was better avoiding overfitting.
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-In order to better improve the the model taken from `Even More Powerful Network` I increased the fully connected layers number of parameters to `200`, `100`, `50`, `1`. 
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-# **Behavioral Cloning** 
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-## Writeup Template
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-### You can use this file as a template for your writeup if you want to submit it as a markdown file, but feel free to use some other method and submit a pdf if you prefer.
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-**Behavioral Cloning Project**
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 The goals / steps of this project are the following:
 * Use the simulator to collect data of good driving behavior
 * Build, a convolution neural network in Keras that predicts steering angles from images