Vaibhav P.'s starred repositories
awesome-leetcode-resources
Awesome LeetCode resources to learn Data Structures and Algorithms and prepare for Coding Interviews.
awesome-gcp-certifications
Google Cloud Platform Certification resources.
tech-interview-handbook
💯 Curated coding interview preparation materials for busy software engineers
awesome-production-machine-learning
A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning
alibi-detect
Algorithms for outlier, adversarial and drift detection
SystemDesignResources
Documenting resources and notes for learning system design.
system-design-primer
Learn how to design large-scale systems. Prep for the system design interview. Includes Anki flashcards.
scikit-multiflow
A machine learning package for streaming data in Python. The other ancestor of River.
machine-learning-for-software-engineers
A complete daily plan for studying to become a machine learning engineer.
reactive-machine-learning-systems
Code from the book Machine Learning Systems
ResourceBank_CV_NLP_MLOPS_2022
This repository offers a goldmine of materials for students of computer vision, natural language processing, and machine learning operations.
yolov5_monocular_camera_ranging
This repository is a project of monocular camera ranging, which object detection frame is yolov5.
Drone_YOLOv5_Detector
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
Data-Science-For-Beginners
10 Weeks, 20 Lessons, Data Science for All!
ML-For-Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
developer-roadmap
Interactive roadmaps, guides and other educational content to help developers grow in their careers.
interview-process-survival
:rainbow: :unicorn: this repository is a interview process guide for developers (web/frontend focused)
nlp-in-python-tutorial
comparing stand up comedians using natural language processing
CVND_Exercises
Exercise notebooks for CVND.
CVPR2024-Papers-with-Code
CVPR 2024 论文和开源项目合集
Udacity-Deep-Learning-Nanodegree
The course is contained knowledge that are useful to work on deep learning as an engineer. Simple neural networks & training, CNN, Autoencoders and feature extraction, Transfer learning, RNN, LSTM, NLP, Data augmentation, GANs, Hyperparameter tuning, Model deployment and serving are included in the course.