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<rss xmlns:itunes="http://www.itunes.com/dtds/podcast-1.0.dtd" xmlns:atom="http://www.w3.org/2005/Atom" xmlns:podcast="https://podcastindex.org/namespace/1.0" xmlns:media="http://search.yahoo.com/mrss/" version="2.0"><channel><title>Émission de podcast sur le cloud computi</title><link>https://www.spreaker.com/podcast/emission-de-podcast-sur-le-cloud-computi--7051078</link><description><![CDATA[Nom du Podcast :Cloud HorizonDescription du Podcast :Cloud Horizon est une émission de podcast consacrée au monde du cloud computing, des infrastructures numériques et des technologies modernes. Chaque épisode explore les services cloud, la cybersécurité, le stockage de données, l’intelligence artificielle, la virtualisation et les innovations qui transforment les entreprises et le quotidien numérique. Avec des analyses simples, des conseils pratiques et des discussions sur les tendances technologiques, ce podcast s’adresse aussi bien aux professionnels de l’informatique qu’aux passionnés de technologie.]]></description><atom:link href="https://www.spreaker.com/show/7051078/episodes/feed" rel="self" type="application/rss+xml"/><language>fr</language><category>Science</category><copyright>Copyright Marthinusbaloyi</copyright><image><url>https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg</url><title>Émission de podcast sur le cloud computi</title><link>https://www.spreaker.com/podcast/emission-de-podcast-sur-le-cloud-computi--7051078</link></image><lastBuildDate>Wed, 17 Jun 2026 13:43:13 +0000</lastBuildDate><itunes:author>Marthinusbaloyi</itunes:author><itunes:owner><itunes:name>Marthinusbaloyi</itunes:name><itunes:email>feeds@spreaker.com</itunes:email></itunes:owner><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:subtitle>Nom du Podcast :Cloud HorizonDescription du Podcast :Cloud Horizon est une émission de podcast consacrée au monde du cloud computing, des infrastructures numériques et des technologies modernes. Chaque épisode explore les services cloud, la...</itunes:subtitle><itunes:summary><![CDATA[Nom du Podcast :Cloud HorizonDescription du Podcast :Cloud Horizon est une émission de podcast consacrée au monde du cloud computing, des infrastructures numériques et des technologies modernes. Chaque épisode explore les services cloud, la cybersécurité, le stockage de données, l’intelligence artificielle, la virtualisation et les innovations qui transforment les entreprises et le quotidien numérique. Avec des analyses simples, des conseils pratiques et des discussions sur les tendances technologiques, ce podcast s’adresse aussi bien aux professionnels de l’informatique qu’aux passionnés de technologie.]]></itunes:summary><itunes:category text="Science"/><itunes:explicit>false</itunes:explicit><itunes:type>episodic</itunes:type><item><title>Premier enseignement sur le cloud</title><link>https://www.spreaker.com/episode/premier-enseignement-sur-le-cloud--72112392</link><description><![CDATA[Le premier enseignement fondamental du cloud computing est simple mais essentiel : le cloud n’est pas une technologie unique, mais un modèle de fourniture de services informatiques à la demande. Cela signifie que, plutôt que d’acheter et de gérer des serveurs physiques, les entreprises accèdent à des ressources informatiques (stockage, puissance de calcul, bases de données, logiciels) via Internet, selon leurs besoins.]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72112392</guid><pubDate>Fri, 02 Jan 2026 10:32:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72112392/auradb_d_couvrez_la_puissance_de_neo4j_dans_le_cloud_ese8guzns7m.mp3" length="49914079" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/7eac920e-16d4-4100-bae1-db152bc55291/7eac920e-16d4-4100-bae1-db152bc55291.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/7eac920e-16d4-4100-bae1-db152bc55291/7eac920e-16d4-4100-bae1-db152bc55291.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/7eac920e-16d4-4100-bae1-db152bc55291/7eac920e-16d4-4100-bae1-db152bc55291.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>Le premier enseignement fondamental du cloud computing est simple mais essentiel : le cloud n’est pas une technologie unique, mais un modèle de fourniture de services informatiques à la demande. Cela signifie que, plutôt que d’acheter et de gérer des...</itunes:subtitle><itunes:summary><![CDATA[Le premier enseignement fondamental du cloud computing est simple mais essentiel : le cloud n’est pas une technologie unique, mais un modèle de fourniture de services informatiques à la demande. Cela signifie que, plutôt que d’acheter et de gérer des serveurs physiques, les entreprises accèdent à des ressources informatiques (stockage, puissance de calcul, bases de données, logiciels) via Internet, selon leurs besoins.]]></itunes:summary><itunes:duration>3120</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item><item><title>From Graph Nodes to Machine-Readable Knowledge</title><link>https://www.spreaker.com/episode/from-graph-nodes-to-machine-readable-knowledge--72563226</link><description><![CDATA[This podcast explores how individual graph nodes can be transformed into meaningful vector representations through knowledge graph embeddings. It discusses embedding techniques, representation learning, link prediction, semantic similarity, and how these methods enable machines to understand and reason over connected data. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on next-generation intelligent systems.]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72563226</guid><pubDate>Sat, 29 Aug 2020 10:15:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72563226/096_from_node_to_knowledge_graph_embeddings_nodes2022_tomaz_bratanic_fh_almq7h_u.mp3" length="36052478" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/93fed003-6c81-4183-998a-e28a42b21125/93fed003-6c81-4183-998a-e28a42b21125.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/93fed003-6c81-4183-998a-e28a42b21125/93fed003-6c81-4183-998a-e28a42b21125.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/93fed003-6c81-4183-998a-e28a42b21125/93fed003-6c81-4183-998a-e28a42b21125.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>This podcast explores how individual graph nodes can be transformed into meaningful vector representations through knowledge graph embeddings. It discusses embedding techniques, representation learning, link prediction, semantic similarity, and how...</itunes:subtitle><itunes:summary><![CDATA[This podcast explores how individual graph nodes can be transformed into meaningful vector representations through knowledge graph embeddings. It discusses embedding techniques, representation learning, link prediction, semantic similarity, and how these methods enable machines to understand and reason over connected data. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on next-generation intelligent systems.]]></itunes:summary><itunes:duration>2254</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item><item><title>Embedding the Graph</title><link>https://www.spreaker.com/episode/embedding-the-graph--72563228</link><description><![CDATA[This podcast explores how individual graph nodes are transformed into dense vector representations through knowledge graph embeddings, enabling machines to better understand relationships within connected data. It discusses representation learning, embedding techniques, similarity computation, link prediction, and how these methods power modern AI applications. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on advanced graph-based intelligence systems.]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72563228</guid><pubDate>Fri, 12 Jun 2020 13:36:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72563228/094_genealogy_with_different_graph_technologies_for_data_collection_and_visualization_nodes2022_m08ci6e6nak.mp3" length="35354093" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d/4a41e8cc-0e0e-4c00-8bf0-9369f7972e6d.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>This podcast explores how individual graph nodes are transformed into dense vector representations through knowledge graph embeddings, enabling machines to better understand relationships within connected data. It discusses representation learning,...</itunes:subtitle><itunes:summary><![CDATA[This podcast explores how individual graph nodes are transformed into dense vector representations through knowledge graph embeddings, enabling machines to better understand relationships within connected data. It discusses representation learning, embedding techniques, similarity computation, link prediction, and how these methods power modern AI applications. Perfect for data scientists, machine learning engineers, AI researchers, and graph practitioners working on advanced graph-based intelligence systems.]]></itunes:summary><itunes:duration>2210</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item><item><title>Java Meets Graphs</title><link>https://www.spreaker.com/episode/java-meets-graphs--72563229</link><description><![CDATA[This podcast explores the major updates and improvements introduced in Neo4j Java Driver 5.0 and how they enhance connectivity with graph databases. It discusses performance upgrades, API changes, session handling, asynchronous capabilities, and best practices for integrating Java applications with Neo4j. Perfect for Java developers, software engineers, and backend architects building scalable graph-powered applications.]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72563229</guid><pubDate>Sat, 13 Apr 2019 22:25:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72563229/095_what_s_new_in_neo4j_java_driver_version_5_0_nodes2022_dmitriy_tverdiakov_wqnycbo8_y0.mp3" length="13185521" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/08129951-0d67-4c16-9c43-aba3c4136254/08129951-0d67-4c16-9c43-aba3c4136254.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/08129951-0d67-4c16-9c43-aba3c4136254/08129951-0d67-4c16-9c43-aba3c4136254.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/08129951-0d67-4c16-9c43-aba3c4136254/08129951-0d67-4c16-9c43-aba3c4136254.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>This podcast explores the major updates and improvements introduced in Neo4j Java Driver 5.0 and how they enhance connectivity with graph databases. It discusses performance upgrades, API changes, session handling, asynchronous capabilities, and best...</itunes:subtitle><itunes:summary><![CDATA[This podcast explores the major updates and improvements introduced in Neo4j Java Driver 5.0 and how they enhance connectivity with graph databases. It discusses performance upgrades, API changes, session handling, asynchronous capabilities, and best practices for integrating Java applications with Neo4j. Perfect for Java developers, software engineers, and backend architects building scalable graph-powered applications.]]></itunes:summary><itunes:duration>825</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item><item><title>From Noise to Knowledge</title><link>https://www.spreaker.com/episode/from-noise-to-knowledge--72563225</link><description><![CDATA[This podcast explores how transformer-based language models and clustering techniques can be used to resolve keyword ambiguity and improve the quality of knowledge graphs. It discusses entity resolution, semantic similarity, text embeddings, unsupervised learning, and methods for transforming noisy data into structured, reliable knowledge. Perfect for AI engineers, NLP practitioners, data scientists, and knowledge graph professionals working with large-scale text and information systems.]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72563225</guid><pubDate>Tue, 24 Apr 2018 10:35:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72563225/093_keyword_disambiguation_using_transformers_and_clustering_to_build_cleaner_knowledge_nodes2022_qpmht83an5u.mp3" length="33761672" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/6050e97e-b3d4-406e-95de-b2ec149d0f0b/6050e97e-b3d4-406e-95de-b2ec149d0f0b.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/6050e97e-b3d4-406e-95de-b2ec149d0f0b/6050e97e-b3d4-406e-95de-b2ec149d0f0b.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/6050e97e-b3d4-406e-95de-b2ec149d0f0b/6050e97e-b3d4-406e-95de-b2ec149d0f0b.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>This podcast explores how transformer-based language models and clustering techniques can be used to resolve keyword ambiguity and improve the quality of knowledge graphs. It discusses entity resolution, semantic similarity, text embeddings,...</itunes:subtitle><itunes:summary><![CDATA[This podcast explores how transformer-based language models and clustering techniques can be used to resolve keyword ambiguity and improve the quality of knowledge graphs. It discusses entity resolution, semantic similarity, text embeddings, unsupervised learning, and methods for transforming noisy data into structured, reliable knowledge. Perfect for AI engineers, NLP practitioners, data scientists, and knowledge graph professionals working with large-scale text and information systems.]]></itunes:summary><itunes:duration>2111</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item><item><title>Introduction to the Async Python Driver</title><link>https://www.spreaker.com/episode/introduction-to-the-async-python-driver--72563227</link><description><![CDATA[This podcast explores the asynchronous Python driver for Neo4j and how it enables high-performance, non-blocking database interactions in modern applications.<br /><br /><br /><br /><br /><br />]]></description><guid isPermaLink="false">https://api.spreaker.com/episode/72563227</guid><pubDate>Mon, 12 Jun 2017 13:36:00 +0000</pubDate><enclosure url="https://dts.podtrac.com/redirect.mp3/api.spreaker.com/download/episode/72563227/097_introduction_to_the_async_python_driver_nodes2022_rouven_bauer_imaqcmxcn28.mp3" length="13545375" type="audio/mpeg"/><podcast:transcript url="https://transcription.spreaker.com/starship/ac6235ab-bb1c-4e99-95de-427b13e51282/ac6235ab-bb1c-4e99-95de-427b13e51282.srt" type="application/x-subrip" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/ac6235ab-bb1c-4e99-95de-427b13e51282/ac6235ab-bb1c-4e99-95de-427b13e51282.txt" type="text/plain" language="fr"/><podcast:transcript url="https://transcription.spreaker.com/starship/ac6235ab-bb1c-4e99-95de-427b13e51282/ac6235ab-bb1c-4e99-95de-427b13e51282.vtt" type="text/vtt" language="fr"/><itunes:author>Marthinusbaloyi</itunes:author><itunes:subtitle>This podcast explores the asynchronous Python driver for Neo4j and how it enables high-performance, non-blocking database interactions in modern applications.</itunes:subtitle><itunes:summary><![CDATA[This podcast explores the asynchronous Python driver for Neo4j and how it enables high-performance, non-blocking database interactions in modern applications.<br /><br /><br /><br /><br /><br />]]></itunes:summary><itunes:duration>847</itunes:duration><itunes:explicit>false</itunes:explicit><itunes:image href="https://d3wo5wojvuv7l.cloudfront.net/images.spreaker.com/nuvolari-assets/orange_square_mic.jpg"/><itunes:episodeType>full</itunes:episodeType></item></channel></rss>
