Madhav Choudhary's starred repositories
djnago-postgres-docker-starter
This is a cookiecutter project for using django and postgres with docker. It has both development and production's configurations.
carbon-lang
Carbon Language's main repository: documents, design, implementation, and related tools. (NOTE: Carbon Language is experimental; see README)
mml-book.github.io
Companion webpage to the book "Mathematics For Machine Learning"
system-design-resources
These are the best resources for System Design on the Internet
90DaysOfDevOps
This repository started out as a learning in public project for myself and has now become a structured learning map for many in the community. We have 3 years under our belt covering all things DevOps, including Principles, Processes, Tooling and Use Cases surrounding this vast topic.
datacamp-python-data-science-track
All the slides, accompanying code and exercises all stored in this repo. 🎈
Every-Open-Source-Programs
Find here every 🎯 Open Source Programs | Internships | Competitions | Fellowships in a systematic way. Most imp. " Deadlines are mentioned for Registration" .
JAVASCRIPT_NOTES
JAVASCRIPT NOTES SHORT AND UNDERSTANDABLE
social-lstm
Social LSTM implementation in PyTorch
OOPS_NOTES
Notes of OOPS (in c++)
DBMS_SQL-Notes
DBMS_SQL Notes
Programming_with_C_and_Cplus-plus
Notes on "Programming with C and C++"
Operating_System
Resources , Notes , Videos of Operating System
Cplus-plus-STL
C++ STL
Resources-for-preparation-Of-Placements
Lecture video links for preparation of Placements
Dynamic-Programming-Notes
Dynamic Programming Notes
RNN-PedestrianTrajectoryPrediction
Prädiktion von zukünftigen Fußgänger-Trajektorien/Bewegungsbahnen mithilfe eines LSTM-Neuronalen Netzes.
Pedestrian-Trajectories-Prediction-with-RNN
Used LSTM and GRU for RNN, KNN for linear regression
SocialLSTM
An implementation of soical lstm for pedestrian movement forecasting.
Scene-LSTM
Data and Code for "Scene-LSTM: A model for human trajectory prediction" (ISVC 2019)
Social_lstm_pedestrian_prediction
Modification of the paper "Social LSTM: Human Trajectory Prediction in Crowded Space" with the insertion of a new way to take into account social information and the consideration of the pedestrian goal.