Catégorie : Machine Learning
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GraphVite A general and high-performance graph embedding system for various applications Designed for CPU-GPU hybrid architecture
https://graphvite.io/
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Getting started with AI? Start here!
https://medium.com/hackernoon/the-decision-makers-guide-to-starting-ai-72ee0d7044df
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How to Deploy Machine Learning Models
https://christophergs.github.io/machine%20learning/2019/03/17/how-to-deploy-machine-learning-models/?fbclid=IwAR0moIFyECj42axMsPZRDVb_eDdITKJKhnVlvG6VBicJWn8UvkDRvUqtzt8
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Map of ethical and rights-based approaches to AI principles
https://ai-hr.cyber.harvard.edu/primp-viz.html
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Advances in Conversational AI
https://ai.facebook.com/blog/advances-in-conversational-ai/
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Profillic: AI research & source code to supercharge your projects
https://www.profillic.com/paper/arxiv:1907.11346
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Introduction to Transformers Architecture
https://rubikscode.net/2019/07/29/introduction-to-transformers-architecture/
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benedekrozemberczki/walklets: A lightweight implementation of Walklets from « Don’t Walk Skip! Online Learning of Multi-scale Network Embeddings » (ASONAM 2017).
https://github.com/benedekrozemberczki/walklets
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How a simple mix of object-oriented programming can sharpen your deep learning prototype
https://towardsdatascience.com/how-a-simple-mix-of-object-oriented-programming-can-sharpen-your-deep-learning-prototype-19893bd969bd
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Executives are not comfortable with analytics platforms, and still prefer their spreadsheets | ZDNet
https://www-zdnet-com.cdn.ampproject.org/c/s/www.zdnet.com/google-amp/article/spreadsheets-still-dominate-business-analytics/
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https://www.intel.ai/transforming-high-dimensional-neural-signals-into-low-dimensional-data-sets/?spredfast-trk-id=sf216257784
https://www.intel.ai/transforming-high-dimensional-neural-signals-into-low-dimensional-data-sets/?spredfast-trk-id=sf216257784
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Which GPU(s) to Get for Deep Learning
https://timdettmers.com/2019/04/03/which-gpu-for-deep-learning/
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GitHub – benedekrozemberczki/GEMSEC: The TensorFlow reference implementation of ‘GEMSEC: Graph Embedding with Self Clustering’ (ASONAM 2019).
https://github.com/benedekrozemberczki/GEMSEC
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DeepMind’s AI learns to generate realistic videos by watching YouTube clips | VentureBeat
https://venturebeat.com/2019/07/19/deepminds-ai-learns-to-generate-realistic-videos-by-watching-youtube-clips/
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GitHub – ahmedbesbes/mrnet: Implementation of the paper: Deep-learning-assisted diagnosis for knee magnetic resonance imaging: Development and retrospective validation of MRNet
https://github.com/ahmedbesbes/mrnet?fbclid=IwAR3jrvdG4enmqm9SwkrPtNCXhsGAa_F7Ey6Fdn69qy1F2-CeD-wFBOg22Qk
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GitHub – subho406/OmniNet: Official Pytorch implementation of « OmniNet: A unified architecture for multi-modal multi-task learning » | Authors: Subhojeet Pramanik, Priyanka Agrawal, Aman Hussain
https://github.com/subho406/OmniNet
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GitHub – deepmind/graph_nets: Build Graph Nets in Tensorflow
https://github.com/deepmind/graph_nets
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Delligence AI
https://delligence.ai/index.html
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Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich
Rules of Machine Learning: Best Practices for ML Engineering by Martin Zinkevich Download: http://martin.zinkevich.org/rules_of_ml/rules_of_ml.pdf
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GitHub – uber-research/plato-research-dialogue-system: This is the Plato Dialogue System, a flexible platform for developing conversational AI agents.
https://github.com/uber-research/plato-research-dialogue-system?fbclid=IwAR0mR2zTQNx8aWU2lAkxfimvuDNUzFLUZZxgd7fhhpT5HQc2-5z9tjb3alc
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GLUE Benchmark
https://gluebenchmark.com/leaderboard
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Natural Language Processing (NLP) Tutorial | NLP Training | Intellipaat – YouTube
https://m.youtube.com/watch?v=KVxIx8f_VpM
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Autocompletion with deep learning
https://tabnine.com/blog/deep
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Neuralink White Paper
https://www.documentcloud.org/documents/6204648-Neuralink-White-Paper.html
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GitHub – huggingface/pytorch-transformers: 👾 A library of state-of-the-art pretrained models for Natural Language Processing (NLP)
https://github.com/huggingface/pytorch-transformers#quick-tour
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Jupyter is the new Excel (but not for your boss) – Towards Data Science
https://towardsdatascience.com/jupyter-is-the-new-excel-but-not-for-your-boss-d24340ebf314
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What is AI – Janhavie – Medium
https://medium.com/@janhavie/what-is-ai-fa68d7ef02b6
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Natural Language Processing (NLP) Techniques for Extracting Information | Search Technologies
https://www.searchtechnologies.com/blog/natural-language-processing-techniques
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Google AI Blog: Multilingual Universal Sentence Encoder for Semantic Retrieval
https://ai.googleblog.com/2019/07/multilingual-universal-sentence-encoder.html?m=1
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Natural Language Processing (NLP) Tutorial | NLP Training | Intellipaat – YouTube
https://m.youtube.com/watch?v=KVxIx8f_VpM
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Google AI Blog: Advancing Semi-supervised Learning with Unsupervised Data Augmentation
https://ai.googleblog.com/2019/07/advancing-semi-supervised-learning-with.html?m=1
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AI Trained On Old Scientific Papers Makes Discoveries Humans Missed – Slashdot
https://m.slashdot.org/story/358084
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GitHub – eng-amrahmed/vanilla-gan-tf2: The Simplest and straightforward Tensorflow 2.0 implementation for vanilla GAN
https://github.com/eng-amrahmed/vanilla-gan-tf2
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GitHub – benedekrozemberczki/awesome-community-detection: A curated list of community detection research papers with implementations.
https://github.com/benedekrozemberczki/awesome-community-detection
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GitHub – AdrianAntico/RemixAutoML: Learn how to use the library in general or across multiple case-study data science courses!
https://github.com/AdrianAntico/RemixAutoML
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Using OpenAPI to Build Smart APIs for Dumb Machines
https://www.infoq.com/articles/openapi/?utm_source=linkedin&utm_medium=link&utm_campaign=calendar
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Introducing Snorkel – Towards Data Science
https://towardsdatascience.com/introducing-snorkel-27e4b0e6ecff
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GitHub – benedekrozemberczki/awesome-graph-embedding: A collection of important graph embedding, classification and representation learning papers with implementations.
https://github.com/benedekrozemberczki/awesome-graph-embedding
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Google AI Blog: Predicting Bus Delays with Machine Learning
https://ai.googleblog.com/2019/06/predicting-bus-delays-with-machine.html?m=1
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New AI programming language goes beyond deep learning | MIT News
http://news.mit.edu/2019/ai-programming-gen-0626
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AI and machine learning will require retraining your entire organization – O’Reilly Media
https://www.oreilly.com/ideas/ai-and-machine-learning-will-require-retraining-your-entire-organization?
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MIT Researchers Open-Source AutoML Visualization Tool ATMSeer
https://www.infoq.com/news/2019/06/open-source-automl-tool/
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Benchmarking Machine Learning on the New Raspberry Pi 4, Model B
https://blog.hackster.io/benchmarking-machine-learning-on-the-new-raspberry-pi-4-model-b-88db9304ce4
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Google AI Blog: Innovations in Graph Representation Learning
https://ai.googleblog.com/2019/06/innovations-in-graph-representation.html?m=1
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DataRobot Acquires MLOps Pioneer and Category Leader, ParallelM | DataRobot
https://www.datarobot.com/news/press/datarobot-acquires-mlops-pioneer-and-category-leader-parallelm/
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Google AI Blog: EfficientNet: Improving Accuracy and Efficiency through AutoML and Model Scaling
https://ai.googleblog.com/2019/05/efficientnet-improving-accuracy-and.html?m=1
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The Best 4-GPU Deep Learning Rig only costs $7000 not $11,000.
http://l7.curtisnorthcutt.com/the-best-4-gpu-deep-learning-rig
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DL_32: (6) Generative Adversarial Network (GAN) : Tensor Flow Implementation in Google Colab
(en) In this presentation, Ahlad Kumar demonstrates the use of a GAN in Google Colab (fr) Dans cette présentation, Ahlad Kumar démontre l’utilisation d’un GAN dans Google Colab (en anglais) https://www.youtube.com/watch?v=ApgSpA-deBY
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