sreshu

sreshu's Stars

• yangshun/tech-interview-handbook

  💯 Curated coding interview preparation materials for busy software engineers

  Language:TypeScript117k2.1k9314.6k

• DataTalksClub/data-engineering-zoomcamp

  Free Data Engineering course!

  Language:Jupyter Notebook24.5k4521255.3k

• ivy-llc/ivy

  Convert Machine Learning Code Between Frameworks

  Language:Python14k7116.9k5.8k

• andkret/Cookbook

  The Data Engineering Cookbook

  13.6k5381352.5k

• jordan-cutler/path-to-senior-engineer-handbook

  All the resources you need to get to Senior Engineer and beyond

  12.9k23071.2k

• serverless/examples

  Serverless Examples – A collection of boilerplates and examples of serverless architectures built with the Serverless Framework on AWS Lambda, Microsoft Azure, Google Cloud Functions, and more.

  Language:JavaScript11.4k2632264.5k

• DataTalksClub/mlops-zoomcamp

  Free MLOps course from DataTalks.Club

  Language:Jupyter Notebook10.9k181932.1k

• prasadgujar/low-level-design-primer

  Dedicated Resources for the Low-Level System Design. Learn how to design and implement large-scale systems. Prep for the system design interview.

  6.3k17022.2k

• data-science-on-aws/data-science-on-aws

  AI and Machine Learning with Kubeflow, Amazon EKS, and SageMaker

  Language:Jupyter Notebook3.3k1192221.1k

• dabit3/awesome-aws-amplify

  Curated list of AWS Amplify Resources

  1.8k883185

• neka-nat/cupoch

  Robotics with GPU computing

  Language:C++89824106108

• Gladiator07/Harvestify

  A machine learning based website that recommends the best crop to grow, fertilizers to use, and the diseases caught by your crops.

  Language:Jupyter Notebook430816387

• awslabs/amazon-kinesis-video-streams-producer-sdk-cpp

  Amazon Kinesis Video Streams Producer SDK for C++ is for developers to install and customize for their connected camera and other devices to securely stream video, audio, and time-encoded data to Kinesis Video Streams.

  Language:C++37352830336

• pytorch/builder

  Continuous builder and binary build scripts for pytorch

  Language:Shell32824133218

• sbvkrishna/low-level-design

  Resources to learn Low-level System Design and prepare for Low-level design (LLD/Machine Coding) Interviews

  2253065

• nicknochnack/MediaPipePoseEstimation

  Language:Jupyter Notebook19344173

• iralabdisco/point_clouds_registration_benchmark

  A benchmark for point clouds registration algorithms

  Language:Python9412119

• awslabs/aws-media-replay-engine

  Media Replay Engine (MRE) is a framework to build automated video clipping and replay (highlight) generation pipelines for live and video-on-demand content.

  Language:Python8171516

• tpounds/mockitopp

  Simple mocking for C++

  Language:C++778196

• aimlnerd/Deploy-machine-learning-model

  Dockerize and deploy machine learning model as REST API using Flask

  Language:Python762040

• jaspereb/UR5_With_ROS_Moveit_Tutorial

  A tutorial on setting up a real UR5 CB arm to run with ROS and Moveit!

  Language:C++44317

• aws-solutions/improving-forecast-accuracy-with-machine-learning

  The Improving Forecast Accuracy with Machine Learning solution generates, tests, compares, and iterates on Amazon Forecast forecasts. The solution automatically produces forecasts and generates visualization dashboards for Amazon QuickSight or Amazon SageMaker Jupyter Notebooks—providing a quick, easy, drag-and-drop interface that displays time series input and forecasted output.

  Language:Python42191424

• ynyeh0221/Codewars

  JavaScript Practice Record

  Language:JavaScript368028

• awsdocs/AWS-Kinesis-Video-Documentation

  Developer guide documentation for Amazon Kinesis Video Streams

  32261428

• immuno121/Automatic-highlight-generation-from-Sports-Videos

  Trained a Deep Learning Model to generate automatically generate highlights from a sports video

  Language:Python31418

• danielstankw/Servoj_RTDE_UR5

  Implementation of servoj() using RTDE in python for YouTube video purposes

  Language:Python27216

• vineeths96/Video-Frame-Prediction

  In this repository, we focus on video frame prediction the task of predicting future frames given a set of past frames. We present an Adversarial Spatio-Temporal Convolutional LSTM architecture to predict the future frames of the Moving MNIST Dataset. We evaluate the model on long-term future frame prediction and its performance of the model on out-of-domain inputs by providing sequences on which the model was not trained.

  Language:Python22207

• aws-samples/amazon-sagemaker-activity-detection

  Language:Python135

• pipelinedb/pipeline_kinesis

  PipelineDB extension for Amazon Kinesis support

  Language:C106211

• aws-samples/aws-ml-readmission-prediction

  Language:Jupyter Notebook42