dkarunakaran/content_filtering

Stars
1
Language
Jupyter Notebook
Created over 1 year ago
Updated over 1 year ago

dkarunakaran/content_filtering

dkarunakaran

There are no reviews yet. Be the first to send feedback to the community and the maintainers!

ROS-node-YOLO-v3-tiny

Object detection using YOLO on Jetson txt2

entity_recoginition_deep_learning

Entity recognition using Deep Learning

robocar

Buillt on top of ROS and donkeycar hardware

Jupyter Notebook

facenet_mtcnn_tensorflow_inference_engine

This is a modified and simplified inference engine of davidsandberg's facenet

carnd-mpc-term2-p5

carnd-vehicle-detection-p5

Jupyter Notebook

rviz_docker

To display rviz that runs on the docker container

scenario_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

Jupyter Notebook

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

Jupyter Notebook

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.

Jupyter Notebook

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

Jupyter Notebook

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).

Jupyter Notebook

dlnd-p1-first-nn

Deep learning nano degreee first project

Jupyter Notebook

carnd-advanced-lane-lines-p4

Jupyter Notebook

askpdf_chatgpt_embeddings

Jupyter Notebook