4_5773660197402707477.pdf
1.8 MB
Simple Deep Learning for
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Programmers Write your own modern neural networks in Keras and Python for images and sequence data
#By: The Lazy Programmer
#book #DL
@Machine_learn
Forwarded from Ramin Mousa
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
Forwarded from Ramin Mousa
1906.06423.pdf
897.7 KB
@Machine_learn #code #paper
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
FixRes is a simple method for fixing the train-test resolution discrepancy. It can improve the performance of any convolutional neural network architecture.
Github: https://github.com/facebookresearch/FixRes
Article:https://arxiv.org/abs/1906.06423
@Machine_learn
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
#code #paper
Y-Autoencoders: disentangling latent representations via sequential-encoding
Article: https://arxiv.org/abs/1907.10949
GitHub: https://github.com/mpatacchiola/Y-AE
arXiv.org
Y-Autoencoders: disentangling latent representations via...
In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)....
@Machine_learn
#CapsuleNet #code
Stacked Capsule Autoencoders
http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
#CapsuleNet #code
Stacked Capsule Autoencoders
http://akosiorek.github.io/ml/2019/06/23/stacked_capsule_autoencoders.htm
@Machine_learn
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
pdf: https://arxiv.org/pdf/1907.13196v1.pdf
Wasserstein Robust Reinforcement Learning
article:https://arxiv.org/abs/1907.13196v1
pdf: https://arxiv.org/pdf/1907.13196v1.pdf
arXiv.org
Wasserstein Robust Reinforcement Learning
Reinforcement learning algorithms, though successful, tend to over-fit to training environments hampering their application to the real-world. This paper proposes $\text{W}\text{R}^{2}\text{L}$ --...
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
#book #python #Scrapy
@Machine_leaen
2_5361938490604913448.pdf
4.3 MB
Learning Scrapy Learn the art of efficient web scraping and crawling with Python
#book #python #Scrapy
@Machine_leaen
#book #python #Scrapy
@Machine_leaen
@Machine_learn
New paper on training with pseudo-labels for semantic segmentation
Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.
Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445
#GCPR2019 #Segmentation #CV
New paper on training with pseudo-labels for semantic segmentation
Semi-Supervised Segmentation of Salt Bodies in Seismic Images:
SOTA (1st place) at TGS Salt Identification Challenge.
Github: https://github.com/ybabakhin/kaggle_salt_bes_phalanx
ArXiV: https://arxiv.org/abs/1904.04445
#GCPR2019 #Segmentation #CV
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
5_6188486461180870748.pdf
10.9 MB
Machine Learning Refined
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Foundations, Algorithms, and Applications
JEREMY WATT, REZA BORHANI, AND AGGELOS K. KATSAGGELOS
#book #ML
@Machine_learn
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
5_6154467025956634927.pdf
27.1 MB
Machine Learning for OpenCV
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
A practical introduction to the world of machine learning and
image processing using #OpenCV and #Python #book #ML
@Machine_learn
@Machine_learn
Interpreting Latent Space of GANs for Semantic Face Editing
https://shenyujun.github.io/InterFaceGAN/
code: https://github.com/ShenYujun/InterFaceGAN.git
Interpreting Latent Space of GANs for Semantic Face Editing
https://shenyujun.github.io/InterFaceGAN/
code: https://github.com/ShenYujun/InterFaceGAN.git
@Machine_learn
How to Implement Progressive Growing GAN Models in Keras
https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/
How to Implement Progressive Growing GAN Models in Keras
https://machinelearningmastery.com/how-to-implement-progressive-growing-gan-models-in-keras/