@Machine_learn

The TensorFlow tutorials are written as Jupyter notebooks and run directly in Google Colab—a hosted notebook environment that requires no setup.

FROM BEGINNERS TO EXPERTS
* Source Codes
* Videos
* Libraries and extensions

https://www.tensorflow.org/tutorials

@Machine_learn

NeRF: Neural Radiance Fields


http://www.matthewtancik.com/nerf

Tensorflow implementation: https://github.com/bmild/nerf

Paper: https://arxiv.org/abs/2003.08934v1

Python Artificial Intelligence Projects for Beginners
#python #book #AI
@Machine_learn

Training with quantization noise for extreme model compression

@Machine_learn

https://ai.facebook.com/blog/training-with-quantization-noise-for-extreme-model-compression/

Paper: https://arxiv.org/abs/2004.07320

GitHub: https://github.com/pytorch/fairseq/tree/master/examples/quant_noise

Reinforcement Learning and Optimal Control
by #book #RL

@Machine_learn

Adversarial Latent Autoencoders (ALAE) not only generate 1024x1024 images with StyleGAN’s quality but also allow to manipulate real-world images in a feed-forward manner. Your move, StyleGAN team!
paper: arxiv.org/abs/2004.04467
code: github.com/podgorskiy/ALAE
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Deep Learning for NLP and Speech Recognition (2019)
#Book #NLP #Speech_Recognition
@Machine_learn

@Machine_learn

TFRT: A new TensorFlow runtime

https://blog.tensorflow.org/2020/04/tfrt-new-tensorflow-runtime.html

@Machine_learn


Combinatorial 3D Shape Generation
via Sequential Assembly

https://arxiv.org/pdf/2004.07414.pdf
https://arxiv.org/abs/2004.07414

@Machine_learn

Reinforcement Learning with Augmented Data

https://mishalaskin.github.io/rad

Code: https://github.com/MishaLaskin/rad

Paper: https://arxiv.org/abs/2004.14990

@Machine_learn

BASNet was already great for salient object detection and background removal.

Repo: https://github.com/NathanUA/U-2-Net

Watsapp: +989333900804
ID: @Machine_learn

deep learning in computer vision
#book #DL
@Machine_learn

@Machine_learn
Global explanations for discovering bias in data

Github: https://github.com/agamiko/gebi

Code: https://github.com/AgaMiko/GEBI/blob/master/notebooks/GEBI.ipynb

Paper: https://arxiv.org/abs/2005.02269v1

Machine Learning A Bayesian and Optimization Perspective
#book #ML
@Machine_learn

Python® Machine Learning
#book #Python
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