Discover @MingalievDinar Open Source projects

Dinar (@MingalievDinar)

MingalievDinar

Stars
19
Global Rank 661,842 (Top 23 %)
Followers 7
Following 2
Registered over 10 years ago
Most used languages

Jupyter Notebook
80.0 %

C++
10.0 %

Python
10.0 %
Location 🇦🇹 Austria
Country Total Rank 2,665
Country Ranking

Jupyter Notebook
94

C++
1,056

Python
1,351

TrafficSign

In this project, I use convolutional neural network to classify traffic signs. Specifically, I trained a model to classify traffic signs from the German Traffic Sign Dataset. I used TensorFlow for model development and trained it on GPU.

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LineDetection

The goal of this project is to create a pipeline that find lines on the road

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sentiment-analysis

The aim - is to develop a model that will give accurate predictions for the customer's test sample, but the training sample for is not given. It should be collected by parsing

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machine-learning

Several Jupyter Notebooks for machine learning

Jupyter Notebook

Self-Driving-Car

Several projects how to program Self-Driving-Cars

VehicleDetectionTracking

The goal is to create a pipeline to identify and track vehicles in a video from a front-facing camera on a car with a traditional Computer Vision approach for object detection: processing stages, feature extraction, spatial sampling and classification

Jupyter Notebook

BehavioralCloning

The goal of the project - to build a neural network (in Keras) which can drive a car in a simulation track.

face-generation

Introduction to Deep Convolutional Generative Adversarial Networks (DCGANs) for Face Generation

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adverity

An introduction of a simple approach for CTR Anomaly Detection

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ExtendedKalmanFilter

The goal of the project is implementation of Extended Kalman filter C++. Be using simulated lidar and radar measurements we detecting a bicycle that travels around your vehicle. The Kalman filter for lidar measurements and radar measurements is used to track the bicycle's position and velocity.

AdvancedLaneDetection

The Goal of this Project is to write a software pipeline to identify the lane boundaries in a video from a front-facing camera on a car. The camera calibration images, test road images, and project videos are available in the project repository.

Jupyter Notebook