Praateek Mahajan (praateekmahajan)

praateekmahajan

Geek Repo

Company:@square

Location:San Francisco, California

Home Page:http://prtk.in

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squareup

Praateek Mahajan's repositories

TrojanNN-Pytorch

Implementation of https://github.com/PurduePAML/TrojanNN

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machinelearningconcepts

A list of tutorials that helped me understand certain concepts.

movie-meta-fetch

Fetch metadata of your movies easily with this script and save them in a html file to view/search for movies according to genre, awards, imdb rating.

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action_recognition_ucf101

So far limited work has been done in using temporal features for feature detection, my aim is to either compare the latest state of the art tools for action recognition or to implement a vanilla solution.

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moving-mnist-vae

Experiments to learn latent representation/physical information about moving mnist dataset.

pycom

Simple Flask Based ECommerce Cart System

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predict-speed-using-dashcam

Trying to predict speed of vehicle using just dashcam (monocular) with CNN style architectures, using Optical Flow as inputs.

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tfrecord

TFRecord reader for PyTorch

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Algorithms_practice

Some practice questions while preparing for interviews.

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cudf

cuDF - GPU DataFrame Library

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gems

Ruby gems driving Octo-matic engine. http://www.octo.ai

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kubernetes-workshop

⚙️ A Gentle introduction to Kubernetes with more than just the basics. 🌟 Give it a star if you like it.

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petastorm

Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code.

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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pytorch-transformers

👾 A library of state-of-the-art pretrained models for Natural Language Processing (NLP)

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spark

Apache Spark - A unified analytics engine for large-scale data processing

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tempo

The purpose of this project is to provide an API for manipulating time series on top of Apache Spark. Functionality includes featurization using lagged time values, rolling statistics (mean, avg, sum, count, etc), AS OF joins, and downsampling & interpolation. This has been tested on TB-scale of historical data and is unit tested for quality purposes.

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xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

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