Documentação da quali sobre segmentações e deep learning.
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Updated
Apr 18, 2024 - TeX
Documentação da quali sobre segmentações e deep learning.
MPhil Project at QUT: Convolutional Neural Networks and Volcano Plots: Screening and Prediction of Two-Dimensional Single-Atom Catalysts
🚀 H2G-Net: Segmentation of breast cancer region from whole slide images
A pipeline for semantic segmentation, densification, and planar flattening for improving voxelization and mesh reconstruction quality of airborne LiDAR data.
Project on extending a Neural Partitioner for the M149 - Database Systems course, NKUA, Spring 2023.
Doctoral thesis Ondrej Pesek
Master's dissertation for breast cancer detection in mammograms using deep learning techniques in Tensorflow. Contains the final report and source code.
Master thesis in Incomplete time-series classification methods
A web application for generating music that sounds like the Grateful Dead.
Project title: Using Deep Learning to predict overall survival times for breast cancer from H&E whole slide biopsy
Bachelor Thesis in Computer and Automation Engineering
LaTeX Bachelor's Thesis
Repository containing my papers on technology.
Presentation about Convolutional Neural Networks (German) as Part of a Proseminar. Summer Term 2022 @ Ulm University.
Article on Convolutional Neural Networks (German) as Part of a Proseminar. Summer Term 2022 @ Ulm University.
Deep learning for pedestrians: backpropagation in CNNs. Latex and PyTorch code to verify theoretical derivations.
This repository will contain my studies and codes for predicting traffic.
Projects from the DeepLearning.AI Specialization. Implementation of algorithms is inspired by research papers on topics ranging from deep fully connected neural networks to ConvNets. In the computer vision part of the course, architectures include ResNets, Inception networks, and others for object detection, semantic segmentation, and face recog…
Machine Learning Practical - Coursework 2 Report: Analysing problems with the VGG deep neural network architectures (with 8 and 38 hidden layers) on the CIFAR100 dataset by monitoring gradient flow during training. And exploring solutions using batch normalization and residual connections.
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