Code Vampire's repositories
-deprecated-NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV
Computer vision container that includes Jupyter notebooks with built-in code hinting, Anaconda, CUDA 11.8, TensorRT inference accelerator for Tensor cores, CuPy (GPU drop in replacement for Numpy), PyTorch, PyTorch geometric for Graph Neural Networks, TF2, Tensorboard, and OpenCV for accelerated workloads on NVIDIA Tensor cores and GPUs.
Machine-Learning-For-Predictive-Lead-Scoring
Predictive Lead Scoring does all the hard work for you by leveraging Machine Learning to provide your sales and marketing team with in-depth customer knowledge and ways to target the hottest and most qualified leads – resulting in saved time and higher revenue streams.
Deep-Learning-Ultra
Open source Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in PyTorch, OpenCV (compiled for GPU), TensorFlow 2 for GPU, PyG and NVIDIA RAPIDS
computer-vision-container
This container is no longer supported, and has been deprecated in favor of: https://github.com/joehoeller/NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV
vuejs-flask-docker
Test driven docker solution using VueJS, Flask REST Plus, PostgresSQL, with swagger, prebuilt authentication+JWT's running on NGINX/https using material ui design
Machine-Learning-for-Industrial-IoT-Applications
Machine Learning for Industrial IoT Applications: Predict how long a part will work before performance degrades Perect for 5G cell phone towers, mining, aerospace, large/heavy equipment, farming, autonomous vehicles and even data centers.
Anaconda-CUDA-Accelerated-TensorFlowGPU-Development-Environment
A reproducible containerized environment with CUDA X, Anaconda, TensorFlow-GPU, Keras-GPU, Dask, and PyCUDA.
full-stack-flask-react-kubernetes
Deploy a Flask-based microservice (along with Postgres and React) to a Kubernetes cluster
nginx-server-neo4j-graph-db
NGINX server for NEO4J Graph Database and NEO4J REST API
bandit-box
BanditBox: A reproducible and portable GPU and TPU (TensorRT) accelerated machine learning container for advanced applications such as NLP, contextual bandits, policy gradient networks, & Deep Q Learning.
machine-learning-predict-customers-next-purchase
Machine Learning to predict a customer's next purchase - Fulfills many use-cases from recommendation systems to loyalty programs.
customer-lifetime-value-contractual-or-non-contractual-relationship
Machine Learning to determine Customer Lifetime Value in a contractual or non-contractual setting.
Machine-Learning-to-Predict-Customer-Loyalty-Trajectory
Customer loyalty is the strength of the relationship a customer has with a business as manifested by customer purchasing more and at high frequency. There are various signal or events related to a customer’s engagement with a business. Some examples are transactions, customer service calls and social media comments.
Algorithmic-Data-Cleaning-with-Pandas
Algorithmic accumulator that walks arrays right (reduceRight) while handling conditions without mutations to variables, no loops, and zero non deterministic code design patterns.
Django2Pro-Container
Docker, Django2.2, NGINX, PostgreSQL, pgAdmin, Python 3.x on https, with support for Dev, QA, & Production environments to build RESTful APIs on. Code updates instantly for quick development: Container hot reloads automatically as you write code!
-Subscription-Customer-or-Regular-Customer
Machine Learning to determine if the customer will be a monthly subscription customer (Like Dollar Shave Club, Spotify, Pandora or a Amazon Prime Member), or will they just remain a regular/free customer?
s3-government
Interface dynamically with s3 buckets on AWS GovCloud - Forked from s3Contents repo to work with government security req's.
Rancher-REST-API-Docs
Documentation on Rancher's REST APIs
SASS-CSS3-Animation
SASS Example with CSS3 Animations
Evolutionary-Context-Graph-Neural-Network-for-Predicting-Foreign-Adversarial-Events
Proof of concept demo with code built on OSINT data to predict social influence prediction (PSYOPS), or other events for warfighters, elections, disease outbreaks, geo-political events, as well as foreign and domestic terror threats. From the perspective of intelligence analysts, information overload is overwhelming. Presenting succinct representations of events and their precursors in the form of summaries is an unmet need. We demonstrate how unified data for graph based neural networks solves DoD’s ability to look 10-400 steps into the future with lead time to react.
node-gdal
Node.js bindings for GDAL (Geospatial Data Abstraction Library)