Name: Travis Davies
Type: User
Company: Shanghai Jiao Tong University
Bio: MC-IT Artificial Intelligence student at University of Melbourne. Interests in data and artificial intelligence.
Location: Melbourne, Australia
Travis Davies's Projects
Imitation learning algorithms with Co-training for Mobile ALOHA: ACT, Diffusion Policy, VINN
A project to create a satellite-deployable computer vision model which can accurately extract areas in Australia which are experiencing bushfires.
a CNN that classifies 75 different species of butterfly images with usage of data augmentation
A simple calculator app with JavaScript, HTML and CSS.
My lecture notes for Computer Vision at The University of Melbourne.
An implementation of the Wheels Burrow Data Compressor in Java
Neural Net that recognises handwritten digits using softmax. Taken from a Kaggle competition.
House Price Predictions using a Kaggle dataset, ML model uses Scikit-learn's Random Forest Regressor
Various traditional machine learning algorithms, written in C++
Converts multiview images of a bird to 3D point cloud
That detects and extracts number plates from images and videos
Various search algorithms in Pacman for finding optimal moves when dealing with adversarial agents, mainly minimax (including alpha beta pruning) and expectimax
Various algorithms for Markov's Decision Process and Reinforcement Learning in a grid world and in Pacman, including Value Iteration, Q Learning and Approximate Q-Learning
This is a few algorithms playing with uninformed and informed search for AI - mainly using BFS, DFS, Uniform Cost Search, and A* Search
Awesome multilingual OCR toolkits based on PaddlePaddle (practical ultra lightweight OCR system, support 80+ languages recognition, provide data annotation and synthesis tools, support training and deployment among server, mobile, embedded and IoT devices)
A topological search algorithm for resizing images without noticeable effects of distortion
A 2D platformer game loosely based off of Super Mario and the Snowy Mountains (my first ever program).
My notes for COMP90051 Statistical Machine Learning at University of Melbourne