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Deep unfolding network for image super-resolution

Deep unfolding network inherits the flexibility of model-based methods to super-resolve blurry, noisy images for different scale factors via a single model, while maintaining the advantages of learning-based methods.

Github: https://github.com/cszn/USRNet

Paper: https://arxiv.org/pdf/2003.10428.pdf

Python Data Visualization
Cookbook Second Edition
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TVR: A Large-Scale Dataset for Video-Subtitle Moment Retrieval

Github: https://github.com/jayleicn/TVRetrieval


PyTorch implementation : https://github.com/jayleicn/TVCaption

Paper: https://arxiv.org/abs/2001.09099v1

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PUBG Data Analysis
https://youtu.be/sah4m-9Il7o

@Machine_learn

Regularizing Meta-Learning via Gradient Dropout

Code: https://github.com/hytseng0509/DropGrad

Paper: https://arxiv.org/abs/2004.05859

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Machine Learning and Data Science free online courses to do in quarantine

A. Beginner courses
1. Machine Learning
2. Machine Learning with Python

B. Intermediate courses
3. Neural Networks and Deep Learning
4. Convolutional Neural Networks

C. Advanced course
5. Advanced Machine Learning Specialization

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Local-Global Video-Text Interactions for Temporal Grounding



Github: https://github.com/JonghwanMun/LGI4temporalgrounding

Paper: https://arxiv.org/abs/2004.07514

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​​In a chord diagram (or radial network), entities are arranged radially as segments with their relationships visualised by arcs that connect them. The size of the segments illustrates the numerical proportions, whilst the size of the arc illustrates the significance of the relationships1.

Chord diagrams are useful when trying to convey relationships between different entities, and they can be beautiful and eye-catching.

https://github.com/shahinrostami/chord

#python

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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

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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
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Training with quantization noise for extreme model compression

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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

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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
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