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/
MachineLearningMastery.com
How to Transform Target Variables for Regression in Python - MachineLearningMastery.com
Data preparation is a big part of applied machine learning. Correctly preparing your training data can mean the difference between mediocre and extraordinary results, even with very simple linear algorithms. Performing data preparation operations, such as…
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/
MachineLearningMastery.com
TensorFlow 2 Tutorial: Get Started in Deep Learning with tf.keras - MachineLearningMastery.com
Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep learning framework developed and maintained by Google. Although using TensorFlow directly can be challenging, the modern 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.
ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
https://ai.googleblog.com/2019/12/albert-lite-bert-for-self-supervised.html
Github: https://github.com/google-research/ALBERT
https://ai.googleblog.com/2019/12/albert-lite-bert-for-self-supervised.html
Github: https://github.com/google-research/ALBERT
research.google
ALBERT: A Lite BERT for Self-Supervised Learning of Language Representations
Posted by Radu Soricut and Zhenzhong Lan, Research Scientists, Google Research Ever since the advent of BERT a year ago, natural language research...
A new open benchmark for speech recognition with limited or no supervision
https://ai.facebook.com/blog/a-new-open-benchmark-for-speech-recognition-with-limited-or-no-supervision/
Code and dataset: https://ai.facebook.com/tools/libri-light
Full paper: https://arxiv.org/abs/1912.07875
https://ai.facebook.com/blog/a-new-open-benchmark-for-speech-recognition-with-limited-or-no-supervision/
Code and dataset: https://ai.facebook.com/tools/libri-light
Full paper: https://arxiv.org/abs/1912.07875
Meta
A new open benchmark for speech recognition with limited or no supervision
Facebook AI has released Libri-light, the largest open source dataset for speech recognition to date. This new benchmark helps researchers pretrain acoustic models to understand speech, with few to no labeled examples.
Speeding up model with fusing batch normalization and convolution
https://learnml.today/speeding-up-model-with-fusing-batch-normalization-and-convolution-3
https://learnml.today/speeding-up-model-with-fusing-batch-normalization-and-convolution-3
Using a Convolutional Neural Network to Play Conway's Game of Life with Keras
https://kylewbanks.com/blog/conways-game-of-life-convolutional-neural-network-keras
GIthub: https://github.com/KyleBanks/conways-gol-cnn
Habr: https://habr.com/ru/post/481544/
@ai_machinelearning_big_data
https://kylewbanks.com/blog/conways-game-of-life-convolutional-neural-network-keras
GIthub: https://github.com/KyleBanks/conways-gol-cnn
Habr: https://habr.com/ru/post/481544/
@ai_machinelearning_big_data
Kyle Banks
Using a Convolutional Neural Network to Play Conway's Game of Life with Keras
The goal of this post is to train a convolutional neural network to properly play Conway’s Game of Life without explicitly teaching it the rules of the game.
Horovod is a distributed deep learning training framework for TensorFlow, Keras, PyTorch, and Apache MXNet
https://github.com/horovod/horovod
https://lfai.foundation/press-release/2018/12/13/lf-deep-learning-welcomes-horovod-distributed-training-framework-as-newest-project/
https://github.com/horovod/horovod
https://lfai.foundation/press-release/2018/12/13/lf-deep-learning-welcomes-horovod-distributed-training-framework-as-newest-project/
GitHub
GitHub - horovod/horovod: Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet.
Distributed training framework for TensorFlow, Keras, PyTorch, and Apache MXNet. - horovod/horovod
RepPoints: Point Set Representation for Object Detection
Github: https://github.com/microsoft/RepPoints
Article: https://arxiv.org/abs/1904.11490
@ai_machinelearning_big_data
Github: https://github.com/microsoft/RepPoints
Article: https://arxiv.org/abs/1904.11490
@ai_machinelearning_big_data
GitHub
GitHub - microsoft/RepPoints: Represent Visual Objects by Point Sets
Represent Visual Objects by Point Sets. Contribute to microsoft/RepPoints development by creating an account on GitHub.
HSE Faculty of Computer Science and Yandex launch registration for the 3rd International Data Analysis Olympiad (IDAO 2020)
⚡️The platinum partner of IDAO 2020 is QIWI Russia
The Olympiad includes 2 parts:
📍Online Stage, 15 January – 11 February2020
📍Offline stage (Final), which will be held on 2–5 April in Yandex office, Moscow.
🌟We are calling for the world’s best teams!
Winners and prize-holders of IDAO 2020 will receive valuable prizes and gifts.
Learn more: https://idao.world/
⚡️The platinum partner of IDAO 2020 is QIWI Russia
The Olympiad includes 2 parts:
📍Online Stage, 15 January – 11 February2020
📍Offline stage (Final), which will be held on 2–5 April in Yandex office, Moscow.
🌟We are calling for the world’s best teams!
Winners and prize-holders of IDAO 2020 will receive valuable prizes and gifts.
Learn more: https://idao.world/
Develop an Intuition for Severely Skewed Class Distributions
https://machinelearningmastery.com/how-to-develop-an-intuition-skewed-class-distributions/
https://machinelearningmastery.com/how-to-develop-an-intuition-skewed-class-distributions/
MachineLearningMastery.com
Develop an Intuition for Severely Skewed Class Distributions - MachineLearningMastery.com
An imbalanced classification problem is a problem that involves predicting a class label where the distribution of class labels in the training dataset is not equal. A challenge for beginners working with imbalanced classification problems is what a specific…
WELCOME TO THE DALI DATASET: a large Dataset of synchronised Audio, LyrIcs and vocal notes.
https://github.com/gabolsgabs/DALI
Paper: https://ismir2018.ircam.fr/doc/pdfs/35_Paper.pdf
Learning Singing From Speech
https://arxiv.org/abs/1912.10128v1
https://github.com/gabolsgabs/DALI
Paper: https://ismir2018.ircam.fr/doc/pdfs/35_Paper.pdf
Learning Singing From Speech
https://arxiv.org/abs/1912.10128v1
GitHub
GitHub - gabolsgabs/DALI: DALI: a large Dataset of synchronised Audio, LyrIcs and vocal notes.
DALI: a large Dataset of synchronised Audio, LyrIcs and vocal notes. - gabolsgabs/DALI
✏️ Multi-Graph Transformer for Free-Hand Sketch Recognition
https://github.com/PengBoXiangShang/multigraph_transformer
Paper: https://arxiv.org/abs/1912.11258v1
https://github.com/PengBoXiangShang/multigraph_transformer
Paper: https://arxiv.org/abs/1912.11258v1
GitHub
GitHub - PengBoXiangShang/multigraph_transformer: IEEE TNNLS 2021, transformer, multi-graph transformer, graph, graph classification…
IEEE TNNLS 2021, transformer, multi-graph transformer, graph, graph classification, sketch recognition, sketch classification, free-hand sketch, official code of the paper "Multi-Graph Tr...