The goal of this project was to try and detect a set of road features in a forward facing vehicle camera data. This is a somewhat naive way as it is mainly using computer vision techniques (no relation to naive Bayesian!). Features we are going to detect and track are lane boundaries and surrounding vehicles.
This is how I built and configured my dedicated data science machine that acts as a remote backend for Jupyter Notebook and PyCharm. It is backed by a powerful Nvidia GPU and is accessible from anywhere, so that when it comes to machine learning tasks I am no longer constrained by my personal computer hardware performance.
The goal of this project was to train a end-to-end deep learning model that would let a car drive itself around the track in a driving simulator. The approach I took was based on a paper by Nvidia research team with a significantly simplified architecture that was optimised for this specific project.
This is my attempt to tackle traffic signs classification problem with a convolutional neural network implemented in TensorFlow (reaching 99.33% accuracy). The highlights of this solution would be data preprocessing, data augmentation, pre-training and skipping connections in the network.
This is a TensorFlow follow-along for an amazing Deep Learning tutorial by Daniel Nouri. Daniel describes ways of approaching a computer vision problem of detecting facial keypoints in an image using various deep learning techniques, while these techniques gradually build upon each other, demonstrating advantages and limitations of each.