OpenCV
Scikit-learn
Pandas
NumPy
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WEKA
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OpenCV Microsoft AzureMicrosoft Azure might be a bit more popular than OpenCV. We know about 64 links to it since March 2021 and only 50 links to OpenCV. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I would use OpenCV or Scikit-Image for image-related tasks. - Source: dev.to / 5 months ago
You might be able to achieve this with scripting tools like AutoHotkey or Python with libraries for GUI automation and image recognition (e.g., PyAutoGUI https://pyautogui.readthedocs.io/en/latest/, OpenCV https://opencv.org/). Source: 5 months ago
- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection. Source: 9 months ago
I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest... Source: 9 months ago
You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++. Source: 11 months ago
Before you start, ensure you have an active Azure subscription, if you don't have one, Click here to create a free account. - Source: dev.to / 25 days ago
A VM is the original “hosting” product of the cloud era. Over the last 20 years, VM providers have come and gone, as have enterprise virtualization solutions such as VMware. Today you can do this somewhere like OVHcloud, Hetzner or DigitalOcean, which took over the “server” market from the early 2000’s. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft's Azure also offer VMs, at a less... - Source: dev.to / 3 months ago
Before deploying the application with Kubernetes, you need to containerize the application using docker. This article shows how to deploy a Flask application on Ubuntu 22.04 using Minikube; a Kubernetes tool for local deployment for testing and free offering. Alternatively, you can deploy your container apps using Cloud providers such as GCP(Google Cloud), Azure(Microsoft) or AWS(Amazon). - Source: dev.to / 3 months ago
Consider cloud storage services for offsite storage and automation (Azure, AWS, GCP). - Source: dev.to / 7 months ago
That is what the YAML is for. Securely send data to a specific cloud service ( AWS, Google Cloud, Azure ). They call the whole process, CI/CD, deployment, etc etc etc. (Hey picky, I know they are not the same, but they kind of are.). - Source: dev.to / 7 months ago
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Amazon AWS - Amazon Web Services offers reliable, scalable, and inexpensive cloud computing services. Free to join, pay only for what you use.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
DigitalOcean - Simplifying cloud hosting. Deploy an SSD cloud server in 55 seconds.
NumPy - NumPy is the fundamental package for scientific computing with Python
Linode - We make it simple to develop, deploy, and scale cloud infrastructure at the best price-to-performance ratio in the market.Sign up to Linode through SaaSHub and get a $100 in credit!