GLOM-TensorFlow VS nodding-pigeon

Compare GLOM-TensorFlow vs nodding-pigeon and see what are their differences.

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GLOM-TensorFlow nodding-pigeon
4 3
36 13
- -
0.0 0.0
about 3 years ago 6 months ago
Python Python
Apache License 2.0 MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

GLOM-TensorFlow

Posts with mentions or reviews of GLOM-TensorFlow. We have used some of these posts to build our list of alternatives and similar projects.

nodding-pigeon

Posts with mentions or reviews of nodding-pigeon. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing GLOM-TensorFlow and nodding-pigeon you can also consider the following projects:

InvoiceNet - Deep neural network to extract intelligent information from invoice documents.

Spectrum - Spectrum is an AI that uses machine learning to generate Rap song lyrics

image-super-resolution - 🔎 Super-scale your images and run experiments with Residual Dense and Adversarial Networks.

Machine-Learning-Collection - A resource for learning about Machine learning & Deep Learning

efficientnet-lite-keras - Keras reimplementation of EfficientNet Lite.

image-quality-assessment - Convolutional Neural Networks to predict the aesthetic and technical quality of images.

wtte-rnn - WTTE-RNN a framework for churn and time to event prediction

cnn-raccoon - Create interactive dashboards for your Convolutional Neural Networks with a single line of code!

ydata-synthetic - Synthetic data generators for tabular and time-series data

head-pose-estimation - Realtime human head pose estimation with ONNXRuntime and OpenCV.

tf2-published-models - Sarus implementation of classical ML models. The models are implemented using the Keras API of tensorflow 2. Vizualization are implemented and can be seen in tensorboard.

Speech_driven_gesture_generation_with_autoencoder - This is the official implementation for IVA '19 paper "Analyzing Input and Output Representations for Speech-Driven Gesture Generation".