BeeGass/Agents

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

Language: Python - Size: 6.72 MB - Last synced: about 2 months ago - Pushed: about 2 years ago - Stars: 2 - Forks: 0

BeeGass/pathml Fork of Dana-Farber-AIOS/pathml

Tools for computational pathology

Size: 110 MB - Last synced: about 2 months ago - Pushed: almost 2 years ago - Stars: 1 - Forks: 0

BeeGass/central-limit-theorem

visualizing central limit theorem using julia

Language: Jupyter Notebook - Size: 13.6 MB - Last synced: about 2 months ago - Pushed: almost 2 years ago - Stars: 0 - Forks: 0

BeeGass/yolov1-real-time-obj-detection Fork of sendeniz/yolov1-real-time-obj-detection

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

Size: 6.95 MB - Last synced: about 2 months ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

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.

Language: Python - Size: 1.2 MB - Last synced: about 2 months ago - Pushed: about 3 years ago - Stars: 1 - Forks: 0

BeeGass/VAEs

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

Language: Python - Size: 62.6 MB - Last synced: about 2 months ago - Pushed: over 2 years ago - Stars: 2 - Forks: 0

BeeGass/Optimization

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

Language: Python - Size: 86.9 KB - Last synced: about 2 months ago - Pushed: over 2 years ago - Stars: 0 - Forks: 0

BeeGass/game-of-life

Language: C - Size: 29.3 KB - Last synced: about 2 months ago - Pushed: 5 months ago - Stars: 0 - Forks: 0

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.

Language: Jupyter Notebook - Size: 6.65 MB - Last synced: about 2 months ago - Pushed: about 3 years ago - Stars: 2 - Forks: 0

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.

Language: Python - Size: 42.1 MB - Last synced: about 2 months ago - Pushed: about 3 years ago - Stars: 26 - Forks: 4

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.

Language: Jupyter Notebook - Size: 176 MB - Last synced: about 2 months ago - Pushed: almost 3 years ago - Stars: 10 - Forks: 2

BeeGass/HiPPO-Jax

Implementing and testing HiPPO and S4

Language: Python - Size: 204 MB - Last synced: about 2 months ago - Pushed: 5 months ago - Stars: 2 - Forks: 0

BeeGass/ssm Fork of sap-ient-ai/ssm

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

Size: 452 KB - Last synced: about 2 months ago - Pushed: 5 months ago - Stars: 0 - Forks: 0

BeeGass/state-spaces Fork of state-spaces/s4

Sequence Modeling with Structured State Spaces

Size: 23.4 MB - Last synced: about 2 months ago - Pushed: over 1 year ago - Stars: 0 - Forks: 0

BeeGass/BeeGass

Size: 123 KB - Last synced: about 2 months ago - Pushed: 6 months ago - Stars: 0 - Forks: 0