Thoughts, ideas, ramblings, and stories about ponies.
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Updated
May 29, 2024 - Markdown
Thoughts, ideas, ramblings, and stories about ponies.
Implementação do Trabalho de conclusão de curso, com o tema definido "Aproximação da cinemática inversa de um robô manipulador didático através de algoritmos de aprendizado de máquina"
This project is an introduction to artificial neural networks thanks to the implementation of a multilayer perceptron.
Includes examples of code I have written both independently and collaboratively.
flexible and extensible implementation of a multithreaded feedforward neural network in Java including popular optimizers, wrapped up in a console user interface
A resource-conscious neural network implementation for MCUs
Implementing neural networks from scratch for a deeper understanding of concepts, featuring a Jupyter notebook with derivative-based implementations.
Cambridge UK temperature forecast python notebooks
Pytorch implementation of various token mixers; Attention Mechanisms, MLP, and etc for understanding computer vision papers and other tasks.
Video Summarization project implemented using ResNeXt-101 with PyTorch's LSTM and an MLP for importance score prediction, Kernal Temporal Segmentation is applied along with Dynamic Programming KnapSack to maximize the importance of frames in the summary generation. The final visual is created using smoothly combining frames with the MoviePy module.
An interactive fantasy map(and template) powered by LeafletJS, CesiumJS, Typescript and Electron
Chess-inspired algorithm for image ranking
A simple implementation of a Multi-Layer Perceptron (MLP) neural network in TypeScript.使用 TypeScript 实现的简单多层感知器(MLP)神经网络。
Программы по дисциплине "Современные методы глубокого машинного обучения" 6 семестра ФИТ НГУ
The final experiment of machine learning 24, spring in HUST.
Lightning fast C++/CUDA neural network framework
KAN meets Gram Polynomials
Machine Learning Project, University of Tehran, Fall 2021
A repository for personal experiments with neural networks and machine learning in Java. Purely for educational purposes. Intentionally dumb and inefficient.
A project exploring the efficacy of MLP and CNN implementations on classifying the Sign Language MNIST dataset.
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