Corporates Going All in on Neural Machine Translation Research
https://slator.com/technology/corporates-going-all-in-on-neural-machine-translation-research/
https://slator.com/technology/corporates-going-all-in-on-neural-machine-translation-research/
Slator
Corporates Going All in on Neural Machine Translation Research | Slator
Long the domain of academia, fundamental research in machine translation has become a battleground for big tech and niche providers.
The Actual Difference Between Statistics and Machine Learning
https://towardsdatascience.com/the-actual-difference-between-statistics-and-machine-learning-64b49f07ea3
https://towardsdatascience.com/the-actual-difference-between-statistics-and-machine-learning-64b49f07ea3
Medium
The Actual Difference Between Statistics and Machine Learning
No, they are not the same. If machine learning is just glorified statistics, then architecture is just glorified sand-castle construction.
Simulated Policy Learning in Video Models
http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html
http://ai.googleblog.com/2019/03/simulated-policy-learning-in-video.html
Googleblog
Simulated Policy Learning in Video Models
VoiceFilter: Targeted Voice Separation by Speaker-Conditioned Spectrogram Masking
https://google.github.io/speaker-id/publications/VoiceFilter/
https://google.github.io/speaker-id/publications/VoiceFilter/
Natural Language Processing
https://www.youtube.com/watch?v=bDxFvr1gpSU
https://www.youtube.com/watch?v=bDxFvr1gpSU
YouTube
Natural Language Processing
Natural Language Processing is a field of Artificial Intelligence dedicated to enabling computers to understand and communicate in human language. NLP is only a few decades old, but we've made significant progress in that time. I'll cover how its changed…
В коре вашего мозга 17 млрд компьютеров
https://habr.com/ru/post/445420/
https://habr.com/ru/post/445420/
Хабр
В коре вашего мозга 17 млрд компьютеров
Нейросеть нейросетей Изображение brentsview под лицензией CC BY-NC 2.0 В мозг поступает информация из внешнего мира, его нейроны получают данные на входе, производят обработку и выдают некий...
Tuning a Multi-Task Pytorch Network on Fate Grand Order
https://towardsdatascience.com/tuning-a-multi-task-fate-grand-order-trained-pytorch-network-152cfda2e086
https://towardsdatascience.com/tuning-a-multi-task-fate-grand-order-trained-pytorch-network-152cfda2e086
Medium
Tuning a Multi-Task Pytorch Network on Fate Grand Order
building and tuning pytorch based multi-task networks
Watch Me Build an AI Startup
https://www.youtube.com/watch?v=NzmoPqte4V4
https://www.youtube.com/watch?v=NzmoPqte4V4
YouTube
Watch Me Build an AI Startup
I'm going to build a medical imaging classification app called SmartMedScan! The potential customers for this app are medical professionals that need to scale and improve the accuracy of their diagnoses using AI. From ideation, to logo design, to integrating…
Why The Data Science Venn Diagram Is Misleading
https://towardsdatascience.com/why-the-data-science-venn-diagram-is-misleading-16751f852063
https://towardsdatascience.com/why-the-data-science-venn-diagram-is-misleading-16751f852063
Medium
Why The Data Science Venn Diagram Is Misleading
Or Why You Should Not Neglect Soft Skills
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
https://www.shortscience.org/paper?bibtexKey=journals/corr/1406.4729
https://www.shortscience.org/paper?bibtexKey=journals/corr/1406.4729
shortscience.org
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition - ShortScience.org
Spatial Pyramid Pooling (SPP) is a technique which allows Convolutional Neural Networks (CNNs) to us...
Confronting Deep Learning Systems: How Much Things Have Changed and How Much We Do Not Know
https://towardsdatascience.com/confronting-deep-learning-systems-how-much-things-have-changed-f067738b728f
https://towardsdatascience.com/confronting-deep-learning-systems-how-much-things-have-changed-f067738b728f
Medium
Confronting Deep Learning Systems: How Much Things Have Changed and How Much We Do Not Know
As companies embrace AI in their business, they are confronting technologies of Deep Learning systems. That is like entering a dark cave…
CS224n: Natural Language Processing with Deep Learning
Stanford / Winter 2019
http://web.stanford.edu/class/cs224n/project.html
Stanford / Winter 2019
http://web.stanford.edu/class/cs224n/project.html
Towards accelerating disaster response with automated analysis of overhead imagery
https://towardsdatascience.com/towards-accelerating-disaster-response-with-automated-analysis-of-overhead-imagery-18c65731eaaf
https://towardsdatascience.com/towards-accelerating-disaster-response-with-automated-analysis-of-overhead-imagery-18c65731eaaf
Medium
Towards accelerating disaster response with automated analysis of overhead imagery
A review of the SpaceNet Challenge for off-nadir building footprint extraction
Trust and interpretability in machine learning
https://towardsdatascience.com/trust-and-interpretability-in-machine-learning-b7be41f01704
https://towardsdatascience.com/trust-and-interpretability-in-machine-learning-b7be41f01704
Medium
Trust and interpretability in machine learning
Do machine learning models always need to be interpretable? Given a choice between an interpretable model that is inaccurate and a…
Как искусственный интеллект меняет науку
https://habr.com/ru/post/445806/
https://habr.com/ru/post/445806/
Хабр
Как искусственный интеллект меняет науку
Новейшие алгоритмы ИИ разбираются в эволюции галактик, подсчитывают функции квантовых волн, открывают новые химические соединения, и прочее. Есть ли что-нибудь в работе учёных, что не получится...
New 3-D printing approach makes cell-scale lattice structures
http://news.mit.edu/2019/3-d-printing-identical-cell-scale-lattice-0325
http://news.mit.edu/2019/3-d-printing-identical-cell-scale-lattice-0325
MIT News | Massachusetts Institute of Technology
New 3-D printing approach makes cell-scale lattice structures
MIT and other researchers have used an extremely fine-scale form of 3-D printing to make scaffolding for biological cultures, which could make it possible to grow cells that are highly uniform in shape and size, and potentially with certain functions.
The Hype cycle of Magic quadrants, Cool vendors and Confused clients
https://towardsdatascience.com/the-hype-cycle-of-magic-quadrants-cool-vendors-and-confused-clients-b8773d64538c
https://towardsdatascience.com/the-hype-cycle-of-magic-quadrants-cool-vendors-and-confused-clients-b8773d64538c
Towards Data Science
The Hype cycle of Magic quadrants, Cool vendors and Confused clients
A review of Gartner and roundup of their recent Analytics Summit