🖇 @Machine_learn
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
Facebook is open-sourcing DLRM — a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware community develop new, more efficient ways to work with categorical data.
fb: https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/
link: https://arxiv.org/abs/1906.03109
Meta
We are open-sourcing a state-of-the-art deep learning recommendation model to help AI researchers and the systems and hardware…
Deep learning with Python Develop Deep Learning models on Theano and Thensorflow using Keras
#book #keras #DL
@Machine_learn
#book #keras #DL
@Machine_learn
5_6133943928459624650.pdf
5.4 MB
Deep learning with Python Develop Deep Learning models on Theano and Thensorflow using Keras
#book #keras #DL
@Machine_learn
#book #keras #DL
@Machine_learn
@Machine_learn
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
#NLP #DL #course
New fast.ai course: A Code-First Introduction to Natural Language Processing
https://www.fast.ai/2019/07/08/fastai-nlp/
Github: https://github.com/fastai/course-nlp
Videos: https://www.youtube.com/playlist?list=PLtmWHNX-gukKocXQOkQjuVxglSDYWsSh9
@Machine_learn
Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
Deep Learning For Real Time Streaming Data With Kafka And Tensorflow
#ODSC #DeepLearning #Tensorflow
https://www.youtube.com/watch?v=HenBuC4ATb0
@Machine_learn
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How to Develop an Information Maximizing GAN (InfoGAN) in Keras
https://machinelearningmastery.com/how-to-develop-an-information-maximizing-generative-adversarial-network-infogan-in-keras/
__________________________
How to Develop an Information Maximizing GAN (InfoGAN) in Keras
https://machinelearningmastery.com/how-to-develop-an-information-maximizing-generative-adversarial-network-infogan-in-keras/
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
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