AWS Deep Learning AMIs
AWS Auto Scaling
IBM Cloud Bare Metal Servers
Faronics Deep Freeze
Amazon Elastic Inference
Amazon Simple Workflow Service (SWF)
Zing
MxToolBox
OpenCV
Scikit-learn
Pandas
NumPy
Dataiku
Exploratory
htm.java
WEKA
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Based on our record, OpenCV seems to be a lot more popular than AWS Deep Learning AMIs. While we know about 50 links to OpenCV, we've tracked only 3 mentions of AWS Deep Learning AMIs. 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.
AWS Deep Learning AMIs can be used to accelerate deep learning by quickly launching Amazon EC2 instances. - Source: dev.to / over 2 years ago
Ok a bit more on topic of your question. Set up a docker locally on your computer, pick a relevant image with all the python stuff and then do pip install -r requirements -t ./dependencies zip it up, upload to S3 and then get it from there and use on the EC2 instance. Or look into using Deep Learning AMIs they should have pytorch installed: https://aws.amazon.com/machine-learning/amis/. Source: almost 3 years ago
Literally nothing stops you from running EC2 instance with GPU and configuring it yourself. There are even AMIs specialized for ML workloads with everything preconfigured and ready to use - https://aws.amazon.com/machine-learning/amis/. Source: almost 3 years ago
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
AWS Auto Scaling - Learn how AWS Auto Scaling monitors your applications and automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost.
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
IBM Cloud Bare Metal Servers - IBM Cloud Bare Metal Servers is a single-tenant server management service that provides dedicated servers with maximum performance.
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Faronics Deep Freeze - Faronics Deep Freeze provides the ultimate workstation protection by preserving the desired computer configuration and settings.
NumPy - NumPy is the fundamental package for scientific computing with Python