BeeGass

Agents

Reproducible results for the various types of reinforcement algorithms I have implemented

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.

BeeGass

central-limit-theorem

visualizing central limit theorem using julia

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.

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.

HiPPO-Jax

Implementing and testing HiPPO and S4

pathml

Tools for computational pathology

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.

VAEs

Reproducible code showing the various types of variational autoencoders I have implemented

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.

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.

BeeGass/Agents

Reproducible results for the various types of reinforcement algorithms I have implemented

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.

BeeGass/HiPPO-Jax

Implementing and testing HiPPO and S4

BeeGass/VAEs

Reproducible code showing the various types of variational autoencoders I have implemented

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.

BeeGass/pathml

Tools for computational pathology

BeeGass/BeeGass

BeeGass/central-limit-theorem

visualizing central limit theorem using julia

BeeGass/game-of-life

BeeGass/Optimization

Reproducible results for the various types of optimization techniques I have implemented

BeeGass/pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

BeeGass/Samba

Official implementation of "Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling"

BeeGass/ssm

HelloWorlds of SSM concepts (Hippo, H3, S4, Mamba etc)

BeeGass/state-spaces

Sequence Modeling with Structured State Spaces

BeeGass/yolov1-real-time-obj-detection

Real-time object detection using YoloV1 in PyTorch on video and webcam feed.