Keras
TensorFlow
PyTorch
Scikit-learn
OpenCV
TFlearn
Clarifai
Microsoft Cognitive Toolkit (Formerly CNTK)
Metabase
Tableau
Microsoft Power BI
Looker
Redash
Grafana
Domo
Grow
MetabaseBased on our record, Keras should be more popular than Metabase. It has been mentiond 32 times since March 2021. 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.
The core model architecture for Magika was implemented using Keras, a popular open source deep learning framework that enables Google researchers to experiment quickly with new models. - Source: dev.to / 21 days ago
As a beginner, I was looking for something simple and flexible for developing deep learning models and that is when I found Keras. Many AI/ML professionals appreciate Keras for its simplicity and efficiency in prototyping and developing deep learning models, making it a preferred choice, especially for beginners and for projects requiring rapid development. - Source: dev.to / 2 months ago
After setting the variables for the environment, the next step is to install dependencies. To use Gemma, KerasNLP is the dependency used. KerasNLP is a collection of natural language processing (NLP) models implemented in Keras and runnable on JAX, PyTorch, and TensorFlow. - Source: dev.to / 3 months ago
Other popular machine learning tools include PyTorch, Keras, and Scikit-learn. PyTorch is an open-source machine learning library developed by Facebook that is known for its ease of use and flexibility. Keras is a high-level neural networks API that is written in Python and is known for its simplicity. Scikit-learn is a machine learning library for Python that is used for data analysis and data mining tasks. - Source: dev.to / about 1 year ago
I'm not aware of anything off-the-shelf, but if you have sufficient programming experience, one way to do this would be to build a large dataset of reference images and pictures and use something like keras to train a convolutional neural network on them. Source: about 1 year ago
I've never used Tableau, but heard a lot of hate about it. However, in my previous role, we were big fans of Metabase (https://metabase.com). You can also self-host it, which was a huge win for us. - Source: Hacker News / 4 months ago
The solution really depends on what sort of problems you are trying to solve and who your customers are. There are a fair few low-code solutions out there for reporting and data visualisation that are great for finance and marketing teams for example. e.g. https://metabase.com/ , https://evidence.dev/ For enterprise processes I'd go with Camunda (solely based on recommendations and not first hand experience).... - Source: Hacker News / about 1 year ago
Metabase | https://metabase.com | REMOTE | Full-time | Backend, Frontend, Full Stack, and DevOps engineers. - Source: Hacker News / over 1 year ago
With a few simple steps, you can deploy Metabase on Microsoft Azure using Azure Container Apps. This process works for any Docker container hosted on Docker Hub, not just Metabase, so you can try it with your containers. - Source: dev.to / almost 2 years ago
Try metabase.com its built with node and uses plugins. Source: about 2 years ago
TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Tableau - Tableau can help anyone see and understand their data. Connect to almost any database, drag and drop to create visualizations, and share with a click.
PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...
Microsoft Power BI - BI visualization and reporting for desktop, web or mobile
Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.
Looker - Looker makes it easy for analysts to create and curate custom data experiences—so everyone in the business can explore the data that matters to them, in the context that makes it truly meaningful.
