cagnurt Goto Github PK
Name: Cagnur Tekerekoglu
Type: User
Bio: Enthusiastic
Location: Istanbul
Name: Cagnur Tekerekoglu
Type: User
Bio: Enthusiastic
Location: Istanbul
This is going to be my blog
Lane Finding Project for Self-Driving Car ND
An implementation of a Convolutional Neural Network (CNN) in NumPy, followed by a PyTorch model trained on the cats and dogs dataset. Test the model with your own dataset.
This repository contains a notebook implementing a fuzzy controller to control the spread of a virus in an artificial world. The goal is to infect and maintain 60% of the bots. The notebook includes all the details, and analysis, including performance metrics, visualizations, and code. Explore dynamics through fuzzy control techniques.
Sparse Matrix Vector Multiplication (SpMV) is one of the High Performance Computing (HPC) problems. In this project, SpMV problem is practices with two assumptions: Sparse Matrix is Symmetric Nine-Banded matrix. and the matrix is in Block Compress Sparse Row Format (BCSR) storage scheme.
Implement four image classification methods (MLP, CNN, Residual Neural Network, EfficientNet with Transfer Learning) on the Natural Images dataset. Code organized with classes, self-explanatory notebook. Videos provided. Utilize Weight and Biases for versioning and tracking model experiments.
This repo presents two important review papers' summary: Hidden Technical Debt in Machine Learning Systems by Sculley et al. Challenges in Deploying Machine Learning: a Survey of Case Studies by Paleyes, Urma, Lawrence
This repository contains a notebook for knowledge distillation on the MNIST dataset. It includes code for training and evaluating student and teacher models. Complete the distillation function, train the models, and analyze results. Explore the impact of distillation on student performance compared to standalone training.
Matrix-Matrix Multiplication Performance Analysis: Serial C++ implementation with index ordering and loop optimizations for efficient BLAS3 operation on dense matrices of varying sizes.
A repository with a notebook template utilizes Weights and Biases (wandb)'s sweep feature for configuration experimentation and provides a class object for easy wandb integration. Optimize your MNIST model with SGD and wandb logging efficiently.
Compress a pretrained CIFAR10 model using PyTorch's pruning techniques. Explore L1 pruning in both structured and unstructured ways with varying rates. Evaluate model size reduction and CPU inference speed improvements. Self-explanatory notebook provided.
Explore model improvements in two notebooks. "OneLayerNetwork.ipynb" investigates changes such as learning rate, optimizers, batch size, and weight visualization. "MoreLayerNetwork.ipynb" adds layers to achieve +95% test accuracy. Discover optimal configurations for enhanced model performance.
This repository contains implementations of an Autoencoder NN and a GAN for image generation using the MNIST dataset. The Autoencoder is implemented from scratch using NumPy, while the GAN is implemented using PyTorch. The Autoencoder model reconstructs images from the dataset, and the GAN generates new images resembling handwritten digits.
This repository contains a collection of notebook tutorials showcasing various scientific computing methods. Each tutorial focuses on a specific topic and provides detailed explanations, code examples, and demonstrations.
Spatiotemporal receptive field (STRF) of retinal ganglion cells
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.