Machine Learning NLP + CV
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مطالب مفید در حوزه های یادگیری ماشین و بینایی کامپیوتر
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Face and hand tracking in the browser with MediaPipe and TensorFlow.js

Here we are recommending two new TensorFlow packages: facemesh and handpose for tracking key landmarks on faces and hands respectively.

Once the packages are installed, you only need to load the model weights and pass in an image to start detecting facial landmarks or tracking hand landmarks:

#TensorFlow #Deep_Learning
#Hand_Tracking #Computer_Vision #Face_Tracking #CV #AI

@ml_nlp_cv
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کتابخانه tf.explain ابزاری است برای درک بهتر رفتار شبکه عصبی که امکان تحلیل گرادیان ها و ترسیم المان های مصور سازی نظیر heatmap ها را میدهد.
همچنین قابل ترکیب با tensorboard و قابل استفاده از طریق tf.keras API هم میباشد.

The library is adapted to the Tensorflow 2.0 workflow, using tf.keras API as possible. It provides:
- Heatmaps Visualizations & Gradients Analysis
- Both off-training and tf.keras.Callback Usages
- Tensorboard Integration

tf-explain respects the new TF2.0 API, and is primarily based on tf.keras when possible. It benefits from the @tf.function decorator which helps to keep support for both eager and graph mode. This allows keeping most algorithms computation time negligible compared to full training.

Algorithms implemented in tf-explain:
- Activations Visualizations
- Grad CAM
- Occlusion Sensitivity
- SmoothGrad

Documentation: https://tf-explain.readthedocs.io/en/latest/
Github: https://github.com/sicara/tf-explain

#tensorflow

@ml_nlp_cv