Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
Projects in Awesome Lists by BeeGass
A curated list of projects in awesome lists by BeeGass .
https://github.com/beegass/self-taught-machine-learning
I have had trouble in the past finding a place where I could learn about statistical learning algorithms, resources as to how to learn them and the code associated with it. This is my attempt at remedying that issue.
lecture linear-regression machine-learning statistical-learning support-vector-machine
Last synced: 03 Oct 2024
https://github.com/beegass/cs-541-deep-learning
CS-541 Deep Learning is a graduate class that teaches both a theoretical and practical approach to deep learning. You will be able to see this in the different homework files in the form of workable code that can be tested as well as proofs and explanations as to where the code is coming from.
artificial-neural-networks convolutional-neural-networks deep-learning deep-neural-networks generative-adversarial-network recurrent-neural-networks
Last synced: 03 Oct 2024
https://github.com/beegass/vaes
Reproducible code showing the various types of variational autoencoders I have implemented
flax flux jax pytorch variational-autoencoder variational-inference
Last synced: 03 Oct 2024
https://github.com/beegass/agents
Reproducible results for the various types of reinforcement algorithms I have implemented
Last synced: 03 Oct 2024
https://github.com/beegass/deep-q-learning
This is my attempt at implementing the paper "Playing Atari with Deep Reinforcement Learning" By Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra and Martin Riedmiller. This is my first attempt at both reading and implementing a research paper.
deep-q-network deep-reinforcement-learning q-learning reinforcement-learning
Last synced: 03 Oct 2024
https://github.com/beegass/ai-project2
In the class CS-4341, Artificial Intelligence, we were tasked to build an agent that could play the game of Gomoku. In order to achieve this the team implemented the MiniMax algorithm, Alpha-beta-pruning as well as our own understanding of evualtion function to facilitate the previous two algorithms. Additionally to aide in the agents compentency the team built, from scratch, a linear neural network.
alpha-beta-pruning artificial-intelligence evaluation-functions gomoku minimax-algorithm
Last synced: 03 Oct 2024
https://github.com/beegass/central-limit-theorem
visualizing central limit theorem using julia
Last synced: 03 Oct 2024
https://github.com/beegass/optimization
Reproducible results for the various types of optimization techniques I have implemented
Last synced: 03 Oct 2024