rameshoswal Goto Github PK

abr-hw-6

algo-and-ds-in-java

alpa

Training and serving large-scale neural networks

angularjs

autotrader

awesome-open-mlops

The Fuzzy Labs guide to the universe of open source MLOps

awesome-rl

Reinforcement learning resources curated

binderhub

Run your code in the cloud, with technology so advanced, it feels like magic!

bioinformatics-algorithms-coursera

Solutions to Programming Assignments for Bioinformatics Algorithms (Part 1) on Coursera.org

c-practise

classify-bio-images-for-protein-localization-using-al

Most proteins localize to specific regions where they perform their biological function. Fluorescent microscopy can reveal the subcellular localization patterns of tagged proteins. The goal of this project is to use active learning to build a classifier that capable of classifying bioimages (encoded as feature vectors) according to subcellular localization patterns. There are three data pools: Easy: A low-noise data pool Moderate: This pool has some noise (labels and features) Difficult: The points in this pool have a larger number of features than those in the easy and moderate pools. Some of these features are irrelevant. Your algorithm will need to perform active learning and feature selection. Each data pool consists of 4120 training images and 1000 test images. Each image is represented as a feature vector (you do not need to do feature extraction yourself). There are 8 subcellular localization patterns: (i) Endosomes; (ii) Lysosomes; (iii) Mitochondria; (iv) Peroxisomes; (v) Actin; (vi) Plasma Membrane; (vii) Microtubules; and (viii) Endoplasmic Reticulum. The data are based on those released by Dr. Nicholas Hamilton for his paper Statistical and visual differentiation of high throughput subcellular imaging, N. Hamilton, J. Wang, M.C. Kerr and R.D. Teasdale, BMC Bioinformatics 2009, 10:94. Select and implement a suitable active learning algorithm and apply it to the training data. Additionally, implement a random learner that selects random images in the training data. Using a budget of 2,500 calls to the oracle, compute and plot the test errors for each algorithm as a function of the number of calls to the oracle. Use the test data to compute the test errors. Repeat this for the easy and moderate data pools. If you are working on a team, or want extra credit, apply your algorithm to the difficult pool as well.

cml

♾️ CML - Continuous Machine Learning | CI/CD for ML

cn_mnist

cookiecutter-pypackage

Cookiecutter template for a Python package.

deep-learning-tutorials

Tutorials on Deep Learning

deepspeed

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

demo-self-driving

Streamlit app demonstrating an image browser for the Udacity self-driving-car dataset with realtime object detection using YOLO.

design-patterns-in-scala

Code examples for my article "Design Patterns in Scala"

django_tutorial

https://docs.djangoproject.com/en/2.0/intro/tutorial01/

dopamine

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.

easy-application

Over 400 software engineering companies that are easy to apply to

faqchatbot

Set of scripts to build a chatbot which will answer based on the FAQs supplied.

faust

Python Stream Processing

forensic-audio-analysis

MLSP - 2017

frame-level-speech-recognition-with-neural-networks

horovod

Distributed training framework for TensorFlow, Keras, and PyTorch.

industry-machine-learning

A curated list of applied machine learning and data science notebooks and libraries across different industries.

kafka-python

Python client for Apache Kafka

kaggle-playground

Preparing for serious Kaggle competitions!

kaggle_homedepot

Turing Test's Solution for Home Depot Product Search Relevance Competition on Kaggle (https://www.kaggle.com/c/home-depot-product-search-relevance)