@Machine_learn
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
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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@Machine_learn
Local-Global Video-Text Interactions for Temporal Grounding
Github: https://github.com/JonghwanMun/LGI4temporalgrounding
Paper: https://arxiv.org/abs/2004.07514
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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@Machine_learn
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
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
@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
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
NeRF: Neural Radiance Fields
http://www.matthewtancik.com/nerf
Tensorflow implementation: https://github.com/bmild/nerf
Paper: https://arxiv.org/abs/2003.08934v1
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
@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
@Machine_learn
A Gentle Introduction to the Fbeta-Measure for Machine Learning
https://machinelearningmastery.com/fbeta-measure-for-machine-learning/
A Gentle Introduction to the Fbeta-Measure for Machine Learning
https://machinelearningmastery.com/fbeta-measure-for-machine-learning/
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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
@Machine_learn
paper: arxiv.org/abs/2004.04467
code: github.com/podgorskiy/ALAE
@Machine_learn
@Machine_learn
TFRT: A new TensorFlow runtime
https://blog.tensorflow.org/2020/04/tfrt-new-tensorflow-runtime.html
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
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
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
BASNet was already great for salient object detection and background removal.
Repo: https://github.com/NathanUA/U-2-Net
@Machine_learn
The Best Deep Learning Papers from the ICLR 2020 Conference
https://neptune.ai/blog/iclr-2020-deep-learning
The Best Deep Learning Papers from the ICLR 2020 Conference
https://neptune.ai/blog/iclr-2020-deep-learning
neptune.ai
Blog - neptune.ai
Blog for ML/AI practicioners with articles about LLMOps. You'll find here guides, tutorials, case studies, tools reviews, and more.