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ML Visualization IDE

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ML Visualization IDE vs numericaal: What are the differences?

ML Visualization IDE: Make powerful, interactive machine learning visualizations. Debug your machine learning models in realtime with powerful, interactive visualizations Quickly log charts from your Python script, visualize your model development in live dashboards, and share interactive plots with your team, in just 2 minutes.; numericaal: Machine learning for mobile & IoT made easy. numericaal automates model optimization and management so you can focus on data and training.

ML Visualization IDE and numericaal belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by ML Visualization IDE are:

  • Powerful, interactive visualizations
  • Quickly log charts
  • Visualize your model development in live dashboards

On the other hand, numericaal provides the following key features:

  • MODEL RESOURCE OPTIMIZATION - We automatically run multiple toolchains to give you the best speed, power and memory tradeoff on every model change.
  • CROSS-PLATFORM MODEL ANALYTICS - We measure on-device speed and power usage to help you evaluate and compare models across hardware platforms.
  • BOTTLENECK IDENTIFICATION - We help you pinpoint performance bottlenecks and focus your model optimization on layers that matter the most.
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What is ML Visualization IDE?

Debug your machine learning models in realtime with powerful, interactive visualizations. Quickly log charts from your Python script, visualize your model development in live dashboards, and share interactive plots with your team, in just 2 minutes.

What is numericaal?

numericaal automates model optimization and management so you can focus on data and training.

Need advice about which tool to choose?Ask the StackShare community!

Jobs that mention ML Visualization IDE and numericaal as a desired skillset
What are some alternatives to ML Visualization IDE and numericaal?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
See all alternatives