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terraform-ansible-k8s-automation

terraform-aws-eks

Terraform module to create an Elastic Kubernetes (EKS) cluster and associated worker instances on AWS

terraform-cdk

Define infrastructure resources using programming constructs and provision them using HashiCorp Terraform

terraform-course-examples

Terraform examples used in a YouTube course

terraform-docs

Generate documentation from Terraform modules in various output formats

terraform-eks

terraform-gke

terraform-iaac

terraform-iacdevops-with-aws-codepipeline

Implement Terraform IAC DevOps for AWS Project with AWS CodePipeline

terraform-jenkins-slaves-on-demand

terraform-kafka-cluster-vamsi

terraform-provider-aws

Terraform AWS provider

terraform-provider-azurerm

Terraform provider for Azure Resource Manager

terraform-provider-google

Terraform Google Cloud Platform provider

terraform-scripts

terraform-series

Terraform manifests for the youtube series

terraform-synopsis-prod

Deploying Ngixng web application using

terraform-up-and-running-code

Code samples for the book "Terraform: Up & Running" by Yevgeniy Brikman

terraform-vpc-rdsdb

Manifest to provision a vpc and an rds databaase

terraform1

terraform_elk

Provision an ELK stack using terraform

terraform_landmark

terraform_scripts-methun

terraformer

CLI tool to generate terraform files from existing infrastructure (reverse Terraform). Infrastructure to Code

terraforming

Export existing AWS resources to Terraform style (tf, tfstate)

terrafrom-scripts

test

tf-jenkins-slaves-on-demand

tomcat

Docker Official Image packaging for Apache Tomcat

transfer-learning-face-anti-spoofing-attack-model

This repository contains the jupyter and mathematica notebooks of the Transfer Learning Model on VGG16, ResNet 50 architecture with ImageNet weights and FaceNet, and also our code proposed of the technique anti spoofing in the paper "Face Liveness Detection Based on Perceptual Image Quality Assessment Features with Multi-scale Analysis". Our project is about a Transfer Learning CNN on VGG16 architecture because it has the best results with an accuracy of 93,016 % in CASIA dataset, 97,321 % MDP dataset and 91,142 % NUAA dataset, we use a "real-time" landmark detection however the transfer learning model and landmark detection don't work together yet (our future work is that these two technologies can work together to reduce errors and improve the accuracy). This also contains the full paper of the "Anti Spoofing Face Detection Technique based on Transfer Learning Convolutional Neural Networks and Real-Time Facial Landmark Detection" to the Latinx in AI - ICML Workshop call.