🎲 Discrete Probability Distributions for Machine Learning
https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
https://machinelearningmastery.com/discrete-probability-distributions-for-machine-learning/
MachineLearningMastery.com
Discrete Probability Distributions for Machine Learning - MachineLearningMastery.com
The probability for a discrete random variable can be summarized with a discrete probability distribution.
Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems,…
Discrete probability distributions are used in machine learning, most notably in the modeling of binary and multi-class classification problems,…
🗣 Using AI-generated questions to train NLP systems
https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/
code: https://github.com/facebookresearch/UnsupervisedQA
paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/
https://ai.facebook.com/blog/research-in-brief-unsupervised-question-answering-by-cloze-translation/
code: https://github.com/facebookresearch/UnsupervisedQA
paper: https://research.fb.com/publications/unsupervised-question-answering-by-cloze-translation/
Facebook
Research in Brief: Unsupervised Question Answering by Cloze Translation
Facebook AI is releasing code for a self-supervised technique that uses AI-generated questions to train NLP systems, avoiding the need for labeled question answering training data.
If you are programmer or a student / graduate or PHD. IF you have basic knowledge of higher mathematics, probability theory and python? If you dream to try yourself in Data Science?
MegaFon announces a competition for participation in the five-day intensive BigDataCamp!
You could become a participant in the training, just go through testing and write a motivation letter.
All details on the website: https://bigdatacamp.megafon.ru/
MegaFon announces a competition for participation in the five-day intensive BigDataCamp!
You could become a participant in the training, just go through testing and write a motivation letter.
All details on the website: https://bigdatacamp.megafon.ru/
Neural networks in NLP are vulnerable to adversarially crafted inputs.
We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:
https://arxiv.org/abs/1909.01492
We show that they can be trained to become certifiably robust against input perturbations such as typos and synonym substitution in text classification:
https://arxiv.org/abs/1909.01492
arXiv.org
Achieving Verified Robustness to Symbol Substitutions via Interval...
Neural networks are part of many contemporary NLP systems, yet their empirical successes come at the price of vulnerability to adversarial attacks. Previous work has used adversarial training and...
Forwarded from Artificial Intelligence
Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs
code: https://github.com/nianticlabs/depth-hints
paper: https://arxiv.org/abs/1909.09051
dataset : https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
code: https://github.com/nianticlabs/depth-hints
paper: https://arxiv.org/abs/1909.09051
dataset : https://lmb.informatik.uni-freiburg.de/resources/datasets/SceneFlowDatasets.en.html
GitHub
GitHub - nianticlabs/depth-hints: [ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation…
[ICCV 2019] Depth Hints are complementary depth suggestions which improve monocular depth estimation algorithms trained from stereo pairs - nianticlabs/depth-hints
Continuous Probability Distributions for Machine Learning
https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
https://machinelearningmastery.com/continuous-probability-distributions-for-machine-learning/
Torchdata is PyTorch oriented library focused on data processing and input pipelines in general
https://github.com/szymonmaszke/torchdata
https://github.com/szymonmaszke/torchdata
GitHub
GitHub - szymonmaszke/torchdatasets: PyTorch dataset extended with map, cache etc. (tensorflow.data like)
PyTorch dataset extended with map, cache etc. (tensorflow.data like) - szymonmaszke/torchdatasets
Creating a Custom TFX Executor
https://medium.com/tensorflow/creating-a-custom-tfx-executor-28edaa3604f6
The ExampleGen TFX Pipeline Component:
https://github.com/tensorflow/tfx/blob/master/docs/guide/examplegen.md#custom-examplegen
video:
https://www.youtube.com/playlist?list=PLQY2H8rRoyvxR15n04JiW0ezF5HQRs_8F
https://medium.com/tensorflow/creating-a-custom-tfx-executor-28edaa3604f6
The ExampleGen TFX Pipeline Component:
https://github.com/tensorflow/tfx/blob/master/docs/guide/examplegen.md#custom-examplegen
video:
https://www.youtube.com/playlist?list=PLQY2H8rRoyvxR15n04JiW0ezF5HQRs_8F
Medium
Creating a Custom TFX Executor
Posted by Kevin Haas, Zhitao Li, and Robert Crowe on behalf of the TFX team
Contributing Data to Deepfake Detection Research
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/ondyari/FaceForensics/
FaceForensics benchmark
https://kaldir.vc.in.tum.de/faceforensics_benchmark/
https://ai.googleblog.com/2019/09/contributing-data-to-deepfake-detection.html
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/ondyari/FaceForensics/
FaceForensics benchmark
https://kaldir.vc.in.tum.de/faceforensics_benchmark/
research.google
Contributing Data to Deepfake Detection Research
Posted by Nick Dufour, Google Research and Andrew Gully, Jigsaw Deep learning has given rise to technologies that would have been thought impossibl...
Using TensorFlow to predict product weight and dimensions
https://medium.com/tensorflow/using-tensorflow-to-predict-product-weight-and-dimensions-8e6af3ab3466
https://medium.com/tensorflow/using-tensorflow-to-predict-product-weight-and-dimensions-8e6af3ab3466
Medium
Using TensorFlow to predict product weight and dimensions
A Guest Post by Rodolfo Bonnin from the Mercado Libre Applied Machine Learning team
Personal online machine learning and neural networks consultations from professionals from leading companies such as Yandex, dbrain, etc.
Experts with real experience in the field of data science are ready to share their knowledge and help you:
- start working with data
- answer questions on meshing
- with compilation of dataset
- understand whether ML / DL will be used effectively for their tasks
and with many other questions.
https://bigxp.ru/categories/konsultacii_po_mashinnomu_obucheniyu_k13?utm_medium=tg
Experts with real experience in the field of data science are ready to share their knowledge and help you:
- start working with data
- answer questions on meshing
- with compilation of dataset
- understand whether ML / DL will be used effectively for their tasks
and with many other questions.
https://bigxp.ru/categories/konsultacii_po_mashinnomu_obucheniyu_k13?utm_medium=tg
A Gentle Introduction to Joint, Marginal, and Conditional Probability
https://machinelearningmastery.com/joint-marginal-and-conditional-probability-for-machine-learning/
https://machinelearningmastery.com/joint-marginal-and-conditional-probability-for-machine-learning/
MachineLearningMastery.com
A Gentle Introduction to Joint, Marginal, and Conditional Probability - MachineLearningMastery.com
Probability quantifies the uncertainty of the outcomes of a random variable. It is relatively easy to understand and compute the probability for a single variable. Nevertheless, in machine learning, we often have many random variables that interact in often…
An open-source python library built to empower developers to build applications and systems with self-contained Deep Learning and Computer Vision capabilities using simple and few lines of code.
https://github.com/OlafenwaMoses/ImageAI
https://github.com/OlafenwaMoses/ImageAI
GitHub
GitHub - OlafenwaMoses/ImageAI: A python library built to empower developers to build applications and systems with self-contained…
A python library built to empower developers to build applications and systems with self-contained Computer Vision capabilities - OlafenwaMoses/ImageAI
DeepMind Measures 7 Capabilities Every AI Should Have
video: https://www.youtube.com/watch?v=zrF5_O92ELQ
📝 The paper "Behaviour Suite for Reinforcement Learning"
https://arxiv.org/abs/1908.03568
code https://github.com/deepmind/bsuite
video: https://www.youtube.com/watch?v=zrF5_O92ELQ
📝 The paper "Behaviour Suite for Reinforcement Learning"
https://arxiv.org/abs/1908.03568
code https://github.com/deepmind/bsuite
YouTube
These Are The 7 Capabilities Every AI Should Have
❤️ Thank you so much for your support on Patreon: https://www.patreon.com/TwoMinutePapers
📝 The paper "Behaviour Suite for Reinforcement Learning" is available here:
https://arxiv.org/abs/1908.03568
https://github.com/deepmind/bsuite
🙏 We would like to…
📝 The paper "Behaviour Suite for Reinforcement Learning" is available here:
https://arxiv.org/abs/1908.03568
https://github.com/deepmind/bsuite
🙏 We would like to…
A Critical Analysis of Biased Parsers in Unsupervised Parsing
https://arxiv.org/abs/1909.09428v1
https://arxiv.org/abs/1909.09428v1
arXiv.org
A Critical Analysis of Biased Parsers in Unsupervised Parsing
A series of recent papers has used a parsing algorithm due to Shen et al.
(2018) to recover phrase-structure trees based on proxies for "syntactic
depth." These proxy depths are obtained from the...
(2018) to recover phrase-structure trees based on proxies for "syntactic
depth." These proxy depths are obtained from the...