| namespace MoqDemo.Tests | |
| { | |
| using System; | |
| using System.Web; | |
| using System.Web.Mvc; | |
| using System.Web.Routing; | |
| using Moq; | |
| using NUnit.Framework; | |
| using HomeController = Controllers.HomeController; |
| // Simulate genetic inheritance of blood type | |
| #include <stdbool.h> | |
| #include <stdio.h> | |
| #include <stdlib.h> | |
| #include <time.h> | |
| // Each person has two parents and two alleles | |
| typedef struct person | |
| { |
I have a couple of AI/ML projects related to mapping things, often conservation related, with remote sensing data. Some details and packages will vary, but the process below describes how I generally approach these types of problems. Some of these tools I have only touched briefly, but I like them, and this is more an outline of how I would like to approach a new project than a retrospective look at my previous work.
We use AWS, so it makes sense to use datasets and services that are already hosted on AWS. The data discovery and loading part of this process would look somewhat different if we were using Azure and Planetary Computer, and very different if we were using GCP and Earth Engine.
All of my analysis will be done using python on an AWS VM in the same region as my data on S3, Probably using VSCode on Sagemaker or [JupyterLab](https://docs.aws.amazo
See how a minor change to your commit message style can make a difference.
Tip
Have a look at git-conventional-commits , a CLI util to ensure these conventions and generate verion and changelogs
