WIP: Scraped europython-2021

europython-2021/category.json

@@ -0,0 +1,3 @@
+{
+  "title": "EuroPython2021"
+}

europython-2021/videos/a-jacob-s-crocquevieille-r-meson-m-levisalles-b-le-bars-tech-for-good.json

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+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Tech for Good: Build the world you want to live in\n[EuroPython 2021 - Talk - 2021-07-29 - Ni]\n[Online]\n\nBy Alexys Jacob, S\u00e9bastien Crocquevieille, Romain Meson, Margaux Levisalles, Brieuc Le Bars\n\n\nThrough its 1000mercis Impacts initiative, Numberly has been actively working with tech and non tech associations over the years for social, environmental and educational causes.\n\nAs part of these commitments, we want to hand over the stage of this talk to some of our close partners.\n- Margaux from Latitudes.cc will introduce you to the various ways technology can be used for general interest.\n- Brieuc from Code Phoenix will share his great experience in coding teaching in prisons to help rehabilitation.\n- Souad from Descodeuses will explain how her association drives more women from disadvantaged neighborhoods in tech jobs.\n\nThis talk is for all of you who are curious to know how to use your skills for a greater good. Join us to hear and share about Tech for Good!\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\n\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 2538,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/R3toQyUoLn4/hqdefault.jpg",
+  "title": "A. Jacob, S. Crocquevieille, R.Meson, M. Levisalles, B. Le Bars - Tech for Good",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=R3toQyUoLn4"
+    }
+  ]
+}

europython-2021/videos/a-mikhalev-d-dukhan-the-pattern-machine-learning-natural-language-processing-meets-vr-ar.json

@@ -0,0 +1,43 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "The Pattern: Machine Learning Natural Language Processing meets VR/AR\n[EuroPython 2021 - Interactive session - 2021-07-28 - Argument-Clinic [Interactive]]\n[Online]\n\nBy Alexander Mikhalev, Dvir Dukhan\n\nOpen-source project \"The Pattern\" is the platform to inspire collaboration for building modern natural language processing tools and techniques and making them practically useful for end-users. In this session, I will walk through creating 3 Machine Learning NLP pipelines and deploying them using Redis and Redis Modules: RedisGears, RedisGraph and RedisAI. \nThe first pipeline demonstrates how to use traditional NLP techniques, such as the Aho-Corasic algorithm to build a knowledge graph from medical literature using a medical thesaurus (UMLS).\nThe second pipeline demonstrates how to build and deploy BERT Question/Answering model and create API for text to speech interface. Leveraging Redis Cluster sharding and capturing Redis Gears \"keymiss\" event to trigger processing of BERT QA in RedisAI.\nThe third pipeline demonstrates how to deploy Google's T5 (text to text transfer transformers) to build summary of each article.\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 3398,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/ZN2NS4_X1AE/hqdefault.jpg",
+  "title": "A. Mikhalev, D. Dukhan - The Pattern: Machine Learning Natural Language Processing meets VR/AR",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=ZN2NS4_X1AE"
+    }
+  ]
+}

europython-2021/videos/abby-carey-developing-flask-applications-for-google-cloud.json

@@ -0,0 +1,43 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Developing Flask Applications for Google Cloud\n[EuroPython 2021 - Talk - 2021-07-30 - Brian]\n[Online]\n\nBy Abby Carey\n\nLearn how to build Flask apps for Cloud Run, one of Google Cloud's serverless platforms. To accelerate the Cloud Run development loop, we'll be using Cloud Code, an IDE plugin that makes working with Cloud Run easy. We'll also cover how to integrate products like Cloud SQL, Pub/Sub, and Secret Manager.\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 1640,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/9vGlPIaX9BE/maxresdefault.jpg",
+  "title": "Abby Carey - Developing Flask Applications for Google Cloud",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=9vGlPIaX9BE"
+    }
+  ]
+}

europython-2021/videos/abhilash-majumder-introduction-to-quantum-deep-learning.json

@@ -0,0 +1,47 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Introduction to Quantum Deep Learning\n[EuroPython 2021 - Talk - 2021-07-28 - Optiver]\n[Online]\n\nBy Abhilash Majumder\n\nIntroduction to Quantum Deep Learning\nAbstract\nThe aim of the lightning talk is to shed light into the field of Quantum computation in the field of Deep Learning. Qubits , which form the fundamental units of quantum computing can be used to create quantum variational circuits which can be placed over traditional deep learning networks to create hybrid quantum-deep learning models. These models not only rely on the gradient convergence properties of general backpropagation technique, but also on the final probabilistic states of the Qubits. Essentially there has been quite a development to optimize the gradient convergence of these hybrid models with the help of Fischer approximation and Natural Gradient Descent.The talk would focus on the importance of Quantum Variational Deep Learning Circuits and how they provide an advantage over traditional Autograd based Circuits. The application of Quantum Variational circuits in the field of Reinforcement Learning as well as NLP would be one of the main points of the talk. There has been sufficient development in the field of quantum computing and this talk aims to throw light on how to exploit the probabilistic states of Qubits to enhance deep learning models.\n\nTopics:\nIntroduction to Quantum Computing and Qubit system\nQuantum Variational Circuits\nCreating Hybrid Circuits (Classical-Quantum-Classical etc.)\nRealizing Performance of Hybrid Circuits\nApplications in the field of Quantum RL and Quantum NLP (research)\nDemocratizing adoption of Quantum Circuits over traditional deep learning circuits\nResources\nResources (slides, repositories) would be added in course of time.\nResources: https://drive.google.com/file/d/1gtCc8JViacFhlV3-XfKlGruh_OqI0ZIa/view?usp=sharing\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 2591,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    },
+    {
+      "label": "https://drive.google.com/file/d/1gtCc8JViacFhlV3-XfKlGruh_OqI0ZIa/view?usp=sharing",
+      "url": "https://drive.google.com/file/d/1gtCc8JViacFhlV3-XfKlGruh_OqI0ZIa/view?usp=sharing"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/p5jOwsnGgwI/hqdefault.jpg",
+  "title": "Abhilash Majumder - Introduction to Quantum Deep Learning",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=p5jOwsnGgwI"
+    }
+  ]
+}

europython-2021/videos/abigail-dogbe-python-in-a-world-of-pan-africanism.json

@@ -0,0 +1,43 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Python in a world of Pan-Africanism\n[EuroPython 2021 - Keynote - 2021-07-28 - Optiver]\n[Online]\n\nBy Abigail Dogbe\n\nThe use of Python in Africa is widely spread daily. In this talk, I will take you on a journey of what Python means to us in a Pan-African setting, lessons learned from organizing PyCon Africa, the people behind it, challenges we are facing and reflections on what works in our ecosystem.\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\n\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 2809,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/ps6XCS8O65s/hqdefault.jpg",
+  "title": "Abigail Dogbe - Python in a world of Pan-Africanism",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=ps6XCS8O65s"
+    }
+  ]
+}

europython-2021/videos/abraham-coiman-wildfire-modeling-in-yosemite-national-park.json

@@ -0,0 +1,43 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Wildfire Modeling in Yosemite National Park\n[EuroPython 2021 - Talk - 2021-07-28 - Parrot [Data Science]]\n[Online]\n\nBy Abraham Coiman\n\nIn this talk, we will show you the use of GRASS GIS (Geographic Resources Analysis Support System - Geographical Information System) and other geospatial Python libraries within a Jupyter Notebook to simulate wildfire spread in Yosemite National Park California USA.  We will also show you a straightforward workflow to obtain and save input geospatial data for wildfire simulation using Google Earth Engine (GEE) Python API, GeoPandas, and geemap (a Python package for interactive mapping with GEE). GRASS GIS commands are generally run into bash shells, thus in this talk, we will demonstrate how we run GRASS GIS commands from Jupyter notebook to model wildfire behavior and display the resulting maps. We expect the audience has a basic understanding of GIS, Remote Sensing, and Python programming.\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 1627,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/WZMmv85HKpg/hqdefault.jpg",
+  "title": "Abraham Coiman - Wildfire Modeling in Yosemite National Park",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=WZMmv85HKpg"
+    }
+  ]
+}

europython-2021/videos/alejandro-saucedo-production-ml-monitoring-outliers-drift-explainers-statistical-performance.json

@@ -0,0 +1,43 @@
+{
+  "copyright_text": "Creative Commons Attribution license (reuse allowed)",
+  "description": "Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance\n[EuroPython 2021 - Talk - 2021-07-30 - Parrot [Data Science]]\n[Online]\n\nBy Alejandro Saucedo\n\nSession Description\n\nThe lifecycle of a machine learning model only begins once it's in production. In this talk we provide a practical deep dive on best practices, principles, patterns and techniques around production monitoring of machine learning models. We will cover standard microservice monitoring techniques applied into deployed machine learning models, as well as more advanced paradigms to monitor machine learning models with Python leveraging advanced monitoring concepts such as concept drift, outlier detector and explainability.\n\nWe'll dive into a hands on example, where we will train an image classification machine learning model from scratch using Tensorflow, deploy it, and introduce advanced monitoring components as architectural patterns with hands on examples. These monitoring techniques will include AI Explainers, Outlier Detectors, Concept Drift detectors and Adversarial Detectors. We will also be understanding high level architectural patterns that abstract these complex and advanced monitoring techniques into infrastructural components that will enable for scale, introducing the standardised interfaces required for us to enable monitoring across hundreds or thousands of heterogeneous machine learning models.\n\nBenefits to ecosystem\n\nThis talk will benefit the ecosystem by providing cross-functional knowledge, bringing together best practices from data scientists, software engineers and DevOps to tackle the challenge of machine learning monitoring at scale. During this talk we will shed light into best practices in the python ecosystem that can be adopted towards production machine learning, and we will provide a conceptual and practical hands on deep dive which will allow the community to both, tackle this issues and help further the discussion.\n\n\nLicense: This video is licensed under the CC BY-NC-SA 4.0 license: https://creativecommons.org/licenses/by-nc-sa/4.0/\nPlease see our speaker release agreement for details: https://ep2021.europython.eu/events/speaker-release-agreement/",
+  "duration": 1541,
+  "language": "eng",
+  "recorded": "2020-07-26",
+  "related_urls": [
+    {
+      "label": "Conference schedule",
+      "url": "https://ep2021.europython.eu/schedule/"
+    },
+    {
+      "label": "Conference Website",
+      "url": "https://ep2021.europython.eu/"
+    },
+    {
+      "label": "https://creativecommons.org/licenses/by-nc-sa/4.0/",
+      "url": "https://creativecommons.org/licenses/by-nc-sa/4.0/"
+    },
+    {
+      "label": "https://ep2021.europython.eu/events/speaker-release-agreement/",
+      "url": "https://ep2021.europython.eu/events/speaker-release-agreement/"
+    }
+  ],
+  "speakers": [
+    "TODO"
+  ],
+  "tags": [
+    "conference",
+    "europython",
+    "europython-2021",
+    "europython-online",
+    "python"
+  ],
+  "thumbnail_url": "https://i.ytimg.com/vi/n0bR0IArJDo/hqdefault.jpg",
+  "title": "Alejandro Saucedo - Production ML Monitoring: Outliers, Drift, Explainers & Statistical Performance",
+  "videos": [
+    {
+      "type": "youtube",
+      "url": "https://www.youtube.com/watch?v=n0bR0IArJDo"
+    }
+  ]
+}