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ROS-node-YOLO-v3-tiny
Object detection using YOLO on Jetson txt2entity_recoginition_deep_learning
Entity recognition using Deep Learningrobocar
Buillt on top of ROS and donkeycar hardwarecarnd-mpc-term2-p5
carnd-vehicle-detection-p5
rviz_docker
To display rviz that runs on the docker containerscenario_extraction_framework
carnd-pid-control-term2-p3
Implement a PID controller in C++ to maneuver the vehicle around the track in simulator.carnd-path-planning-term3-p1
kalman_filtering_finance
dlnd-p2-image-classification
In this project, you'll classify images from the CIFAR-10 dataset.carnd-kidnapped-vehicle-term2-p3
scenario_based_falsification
collaborative_filtering
carnd-behavioral-cloning-p3
This is the third project of the Udacity Selfdriving Car nanodegree.The project is about to train the car to almost go around the track. I have used modified NVIDIA architecture and different data augumentation technique to train the model.carnd-traffic-sign-classifier-p2
In this project, I've used deep neural networks and convolutional neural networks to classify traffic signs. then it has been trained and validated a model so it can classify traffic sign images using the German Traffic Sign Dataset.carnd-extended-kalman-filter-term2-p1
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by using Bayesian inference and estimating a joint probability distribution over the variables for each timeframe.carnd-semantic-segmentation-term3-p2
dlnd-other
carnd-lanelines-p1
In this project, you will be writing code to identify lane lines on the road, first in an image, and later in a video stream (really just a series of images).dlnd-p1-first-nn
Deep learning nano degreee first projectcontent_filtering
carnd-advanced-lane-lines-p4
askpdf_chatgpt_embeddings
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