PyTorch adds new tools and libraries, welcomes
https://pytorch.org/blog/pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community/
NeurIPS 2019 Expo Workshop
https://nips.cc/ExpoConferences/2019/schedule?workshop_id=16
PyTorch Elastic : https://github.com/pytorch/elastic
@ai_machinelearning_big_data
https://pytorch.org/blog/pytorch-adds-new-tools-and-libraries-welcomes-preferred-networks-to-its-community/
NeurIPS 2019 Expo Workshop
https://nips.cc/ExpoConferences/2019/schedule?workshop_id=16
PyTorch Elastic : https://github.com/pytorch/elastic
@ai_machinelearning_big_data
PyTorch
PyTorch adds new tools and libraries, welcomes Preferred Networks to its community
PyTorch continues to be used for the latest state-of-the-art research on display at the NeurIPS conference next week, making up nearly 70% of papers that cite a framework. In addition, we’re excited to welcome Preferred Networks, the maintainers of the Chainer…
Develop an Intuition for Bayes Theorem With Worked Examples
https://machinelearningmastery.com/intuition-for-bayes-theorem-with-worked-examples/
https://machinelearningmastery.com/intuition-for-bayes-theorem-with-worked-examples/
KingSoft WPS: document image dewarping based on TensorFlow
https://blog.tensorflow.org/2019/12/kingsoft-wps-document-image-dewarping.html
https://blog.tensorflow.org/2019/12/kingsoft-wps-document-image-dewarping.html
blog.tensorflow.org
KingSoft WPS: document image dewarping based on TensorFlow
The TensorFlow blog contains regular news from the TensorFlow team and the community, with articles on Python, TensorFlow.js, TF Lite, TFX, and more.
Fairness Indicators: Scalable Infrastructure for Fair ML Systems
http://ai.googleblog.com/2019/12/fairness-indicators-scalable.html
Github: https://github.com/tensorflow/fairness-indicators
Blog: https://blog.tensorflow.org/2019/12/fairness-indicators-fair-ML-systems.html
http://ai.googleblog.com/2019/12/fairness-indicators-scalable.html
Github: https://github.com/tensorflow/fairness-indicators
Blog: https://blog.tensorflow.org/2019/12/fairness-indicators-fair-ML-systems.html
Googleblog
Fairness Indicators: Scalable Infrastructure for Fair ML Systems
Model-Based Reinforcement Learning:
Theory and Practice
https://bair.berkeley.edu/blog/2019/12/12/mbpo/
Theory and Practice
https://bair.berkeley.edu/blog/2019/12/12/mbpo/
The Berkeley Artificial Intelligence Research Blog
Model-Based Reinforcement Learning:
Theory and Practice
Theory and Practice
The BAIR Blog
Tune Hyperparameters for Classification Machine Learning Algorithms
https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/
https://machinelearningmastery.com/hyperparameters-for-classification-machine-learning-algorithms/
Develop Smaller Speech Recognition Models with NVIDIA’s NeMo Framework
https://devblogs.nvidia.com/develop-smaller-speech-recognition-models-with-nvidias-nemo-framework/
Github: https://github.com/NVIDIA/NeMo
https://ngc.nvidia.com/catalog/models/nvidia:quartznet15x5
https://devblogs.nvidia.com/develop-smaller-speech-recognition-models-with-nvidias-nemo-framework/
Github: https://github.com/NVIDIA/NeMo
https://ngc.nvidia.com/catalog/models/nvidia:quartznet15x5
NVIDIA Technical Blog
Develop Smaller Speech Recognition Models with NVIDIA’s NeMo Framework | NVIDIA Technical Blog
As computers and other personal devices have become increasingly prevalent, interest in conversational AI has grown due to its multitude of potential applications in a variety of situations.
Quality-Diversity optimisation algorithms
https://quality-diversity.github.io/
Code: https://gitlab.com/leo.cazenille/qdpy
https://quality-diversity.github.io/
Code: https://gitlab.com/leo.cazenille/qdpy
Quality-Diversity optimisation algorithms
About
This webpage intends to list papers related to QD algorithms, links to tutorials and workshops, and pointers to existing implementations of QD algorithms.
BMW shares AI algorithms used in production, available on GitHub
https://www.bmwblog.com/2019/12/13/bmw-shares-ai-algorithms-used-in-production-available-on-github/
Github : https://github.com/BMW-InnovationLab
https://www.bmwblog.com/2019/12/13/bmw-shares-ai-algorithms-used-in-production-available-on-github/
Github : https://github.com/BMW-InnovationLab
BMW BLOG
BMW shares AI algorithms used in production, available on GitHub
BMW has been implementing next-level production processes throughout its plants around the world for quite some time now. From robots carrying parts
How to Transform Target Variables for Regression With Scikit-Learn
https://machinelearningmastery.com/how-to-transform-target-variables-for-regression-with-scikit-learn/
https://machinelearningmastery.com/how-to-transform-target-variables-for-regression-with-scikit-learn/
T5: Text-To-Text Transfer Transformer
Github: https://github.com/google-research/text-to-text-transfer-transformer
Paper: https://arxiv.org/abs/1910.10683
@ai_machinelearning_big_data
Github: https://github.com/google-research/text-to-text-transfer-transformer
Paper: https://arxiv.org/abs/1910.10683
@ai_machinelearning_big_data
GitHub
GitHub - google-research/text-to-text-transfer-transformer: Code for the paper "Exploring the Limits of Transfer Learning with…
Code for the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" - google-research/text-to-text-transfer-transformer
Arithmetic, Geometric, and Harmonic Means for Machine Learning
https://machinelearningmastery.com/arithmetic-geometric-and-harmonic-means-for-machine-learning/
https://machinelearningmastery.com/arithmetic-geometric-and-harmonic-means-for-machine-learning/
MachineLearningMastery.com
Arithmetic, Geometric, and Harmonic Means for Machine Learning - MachineLearningMastery.com
Calculating the average of a variable or a list of numbers is a common operation in machine learning. It is an operation you may use every day either directly, such as when summarizing data, or indirectly, such as a smaller step in a larger procedure when…
Forwarded from Artificial Intelligence
Results for Standard Classification and Regression Machine Learning Datasets
https://machinelearningmastery.com/results-for-standard-classification-and-regression-machine-learning-datasets/
@ArtificialIntelligencedl
https://machinelearningmastery.com/results-for-standard-classification-and-regression-machine-learning-datasets/
@ArtificialIntelligencedl
MachineLearningMastery.com
Best Results for Standard Machine Learning Datasets - MachineLearningMastery.com
It is important that beginner machine learning practitioners practice on small real-world datasets.
So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be…
So-called standard machine learning datasets contain actual observations, fit into memory, and are well studied and well understood. As such, they can be…
Improving Out-of-Distribution Detection in Machine Learning Models
https://ai.googleblog.com/2019/12/improving-out-of-distribution-detection.html
Dataset: https://github.com/google-research/google-research/tree/master/genomics_ood
Paper: https://arxiv.org/abs/1906.02845
@ai_machinelearning_big_data
https://ai.googleblog.com/2019/12/improving-out-of-distribution-detection.html
Dataset: https://github.com/google-research/google-research/tree/master/genomics_ood
Paper: https://arxiv.org/abs/1906.02845
@ai_machinelearning_big_data
research.google
Improving Out-of-Distribution Detection in Machine Learning Models
Posted by Jie Ren, Research Scientist, Google Research and Balaji Lakshminarayanan, Research Scientist, DeepMind Successful deployment of machine...
Best of Machine Learning in 2019: Reddit Edition
https://heartbeat.fritz.ai/best-of-machine-learning-in-2019-reddit-edition-5fbb676a808
https://heartbeat.fritz.ai/best-of-machine-learning-in-2019-reddit-edition-5fbb676a808
Fritz ai
Best of Machine Learning in 2019: Reddit Edition - Fritz ai
To help sift through some of the incredible projects, research, demos, and more in 2019, here’s a look at 17 of the most popular and talked-about projects in machine learning, curated from the r/MachineLearning subreddit. I hope you find something… Continue…
TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras
https://machinelearningmastery.com/tensorflow-tutorial-deep-learning-with-tf-keras/
https://machinelearningmastery.com/tensorflow-tutorial-deep-learning-with-tf-keras/
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
https://eng.uber.com/generative-teaching-networks/
Paper: https://arxiv.org/abs/1912.07768
https://eng.uber.com/generative-teaching-networks/
Paper: https://arxiv.org/abs/1912.07768
Uber Blog
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data | Uber Blog
Generative Teaching Networks (GANs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.
VizSeq: A visual analysis toolkit for accelerating text generation research
https://ai.facebook.com/blog/vizseq-a-visual-analysis-toolkit-for-accelerating-text-generation-research/
Code: https://github.com/facebookresearch/vizseq
Learn more about Vizseq :
https://facebookresearch.github.io/vizseq/
https://ai.facebook.com/blog/vizseq-a-visual-analysis-toolkit-for-accelerating-text-generation-research/
Code: https://github.com/facebookresearch/vizseq
Learn more about Vizseq :
https://facebookresearch.github.io/vizseq/
Facebook
VizSeq: A visual analysis toolkit for accelerating text generation research
VizSeq is a Python toolkit that provides a scalable solution for visual analysis on text generation tasks.