Roshrini

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

aws-codebuild-samples

Utilities and samples for building on CodeBuild

CountVectorizerApp

Insight Data Engineering Project

deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

dynamic-training-with-apache-mxnet-on-aws

Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.

Insights_Coding_Challenge

Calculate Average degree if hashtag graph generated from twitter data in sliding window

JavaPractice

Java Interview questions practice

mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

mxnet-notebooks

Notebooks for MXNet

NLP_question_answering_system_project

NLP Rule based question answering system

Roshrini/mxnet-notebooks

Notebooks for MXNet

Roshrini/amazon-sagemaker-examples

Example notebooks that show how to apply machine learning and deep learning in Amazon SageMaker

Roshrini/aws-codebuild-samples

Utilities and samples for building on CodeBuild

Roshrini/CountVectorizerApp

Insight Data Engineering Project

Roshrini/deep-learning-containers

AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet.

Roshrini/dynamic-training-with-apache-mxnet-on-aws

Dynamic training with Apache MXNet reduces cost and time for training deep neural networks by leveraging AWS cloud elasticity and scale. The system reduces training cost and time by dynamically updating the training cluster size during training, with minimal impact on model training accuracy.

Roshrini/flink

Mirror of Apache Flink - Insight Data Engineering Project (Implemented CountVectorizer)

Roshrini/lightning-wordcloud-new

Roshrini/mpi-operator

Kubernetes Operator for Allreduce-style Distributed Training

Roshrini/mxnet

Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more

Roshrini/tensorflow-onnx

Convert TensorFlow models to ONNX

Roshrini/benchmark_scala

Roshrini/deep-learning-benchmark-mirror

This is mirror of deep-learning-benchmark

Roshrini/deeplearning-benchmark

Roshrini/deeplearning-cfn

CFN cluster for DeepLearning AMIs.

Roshrini/DeepLearningExamples

Deep Learning Examples

Roshrini/examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

Roshrini/machine-learning-using-k8s

Train and Deploy Machine Learning Models on Kubernetes using Amazon EKS

Roshrini/mf-spark

Matrix Factorization ALS Algorithm

Roshrini/multi-model-server

Multi Model Server is a tool for serving neural net models for inference

Roshrini/mxnet-scala-mf

Roshrini/onnx

Open Neural Network Exchange

Roshrini/onnx-caffe2

Caffe2 implementation of Open Neural Network Exchange (ONNX)

Roshrini/onnx-mxnet

ONNX model format support for Apache MXNet

Roshrini/onnx-tensorflow

Tensorflow Backend for ONNX

Roshrini/sagemaker-python-sdk

A library for training and deploying machine learning models on Amazon SageMaker

Roshrini/sagemaker-pytorch-container

Docker container for running PyTorch scripts to train and host PyTorch models on SageMaker

Roshrini/training_results_v0.7

Roshrini/transformers

🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.

Roshrini/tutorials

Tutorials for using ONNX