lokeshbonta Goto Github PK

academic-projects

Machine Learning, Deep Learning and Data Structure and Algo related stuff will be found here

amdrpp-scripts

batch-processing-trail

cloud-data-analysis-at-scale

[Course-2020-2022] taught at Duke MIDS. This is also a Coursera Course that covers MLOps, ML Engineering and the foundations of Cloud Computing for Data Science.

cmake-tutorial

A Brief Beginner’s Guide to CMake or How to quickly get up and running with CMake

cnn

cplusplus

This repository contains some material I've created to teach courses on C++ and the Standard Template Library (STL). Please see http://www.dre.vanderbilt.edu/~schmidt/cs251 for more information on these topics, including video presentations and slides.

dilated-residual-networks

gan-tutorial

gpu-and-high-performance-parallel-programming

gtest

Just a simple test of the google C++ unit test framework (GTest)

linux_based_tutorials

lokeshbonta

Config files for my GitHub profile.

lokeshbonta.github.io

A Bootstrap 4 resume/CV theme created by Start Bootstrap

machine-learning

machine-learning-course

:speech_balloon: Machine Learning Course with Python. Refer to the course page for step-by-step explanations.

miopen

AMD's Machine Intelligence Library

misc

mivisionx

MIVisionX toolkit is a set of comprehensive computer vision and machine intelligence libraries, utilities, and applications bundled into a single toolkit. Khronos OpenVX is also delivered with MIVisionX.

numpy-ml

Machine learning, in numpy

nvidia-opencl-examples

opencv

A maintained patch branch of OpenCV. GO HERE:

openvx_tutorial

Khronos OpenVX Tutorial Material

protobuf

Protocol Buffers - Google's data interchange format

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rnn-tutorial

rpp

Radeon performance primitives(RPP) libaray is a comprehensive high performance computer vision library for AMD(CPU and GPU) with HIP and OpenCL back-ends.

sample_hip_hsaco

sample repo for verifying HSACO loading in HIP

statistical-learning

Statistical Learning

technical-discussion

This is demo for sharing ideas for coursera building computing course