Guru Shetti's repositories
azure-ai-search-sap-genai-hub
Adapted from Azure demo
CXChat
Ask questions about any data source by leveraging langchains
llama2
Inference code for LLaMA models
vid2vid
Pytorch implementation of our method for high-resolution (e.g. 2048x1024) photorealistic video-to-video translation.
Carnd-Project14-Capstone-Programming-a-Real-Self-Driving-Car
ROS nodes to implement core functionality of the autonomous vehicle system, including traffic light detection, control, and waypoint following! You will test your code using a simulator, and when you are ready, your group can submit the project to be run on Carla.
Carnd-Project10-Model-Predictive-Control
Model Predictive Control to drive the car around the track. This time however you're not given the cross track error, you'll have to calculate that yourself! Additionally, there's a 100 millisecond latency between actuations commands on top of the connection latency.
Carnd-Project8-Kidnapped-Vehicle
implement a 2 dimensional particle filter in C++. Your particle filter will be given a map and some initial localization information (analogous to what a GPS would provide). At each time step your filter will also get observation and control data.
Carnd-Project6-Unscented-Kalman-Filters
Kalman filters have the same three steps: Initialization Prediction Update A standard Kalman filter can only handle linear equations. Both the extended Kalman filter and the unscented Kalman filter allow you to use non-linear equations; the difference between EKF and UKF is how they handle non-linear equations. But the basics are the same: initialize, predict, update.
Carnd-Project5-Vehicle-Detection-and-Tracking
write a software pipeline to identify vehicles in a video from a front-facing camera on a car. The test images and project video are available in the project repository.
Carnd-Project4-Advanced-Lane-Finding
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.
Carnd-Project3-Behavioral-Cloning
model.py - The script used to create and train the model. drive.py - The script to drive the car. You can feel free to resubmit the original drive.py or make modifications and submit your modified version. model.h5 - The saved model. Here is the documentation explaining how to create this file. writeup_report as a markdown or pdf file. It should explain the structure of your network and training approach. The writeup must also include examples of images from the dataset in the discussion of the characteristics of the dataset. While we recommend using English for good practice, writing in any language is acceptable (reviewers will translate). There is no minimum word count so long as there are complete descriptions of the problems and the strategies. See the rubric and the writeup_template.md for more details about the expectations. video.mp4 - A video recording of your vehicle driving autonomously at least one lap around the track.
Carnd-Project2-Traffic-Sign-Classifier
Used deep neural networks and convolutional neural networks to classify traffic signs. Specifically, trained a model to classify traffic signs from the German Traffic Sign Dataset.
Carnd-Project9-PID-Controller
implement a PID controller in C++ to maneuver the vehicle around the track
Carnd-Project1-Finding-Lane-Lines-on-the-Road
dentify lane lines on the road, first in an image, and later in a video stream (really just a series of images).
beacon-ml
Evaluate web beacon metrics for impact on business metrics
TE
thousandeyes script
ios-nanodegree-uikit-2.0
Course repo for UIKit Fundamentals, includes updates for Swift 2.0
catalog
catalog for lsc
AirFloat-1
Implementation of AirPlay audio (AirTunes) for iOS.