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    Explore " machine-learning" with insightful episodes like "Keras: Simplifying Deep Learning with a High-Level API", "Parametrisch ontwerpen versnelt bouwproces", "55. O Machine-Learningu i rozwiązaniach Data-Driven dla bankowości z Piotrem Gawrysiakiem", "Questioning MLOps with Lak Lakshmanan" and "Questioning MLOps with Lak Lakshmanan" from podcasts like """The AI Chronicles" Podcast", "De Innovatiegolf", "Better Software Design", "Pipeline Conversations" and "Pipeline Conversations"" and more!

    Episodes (48)

    Keras: Simplifying Deep Learning with a High-Level API

    Keras: Simplifying Deep Learning with a High-Level API

    Keras is an open-source neural network library written in Python, designed to enable fast experimentation with deep learning algorithms. Conceived by François Chollet in 2015, Keras acts as an interface for the TensorFlow library, combining ease of use with flexibility and empowering users to construct, train, evaluate, and deploy machine learning (ML) models efficiently. Keras has gained widespread popularity in the AI community for its user-friendly approach to deep learning, offering a simplified, modular, and composable approach to model building and experimentation.

    Applications of Keras

    Keras has been employed in a myriad of applications across various domains, demonstrating its versatility and power:

    Advantages of Using Keras

    • Ease of Use: Keras's API is intuitive and user-friendly, making it accessible to newcomers while also providing depth for expert users.
    • Community and Support: Keras benefits from a large, active community, offering extensive resources, tutorials, and support.
    • Integration with TensorFlow: Keras models can tap into TensorFlow's ecosystem, including advanced features for scalability, performance, and production deployment.

    Conclusion: Accelerating Deep Learning Development

    Keras stands out as a pivotal tool in the deep learning ecosystem, distinguished by its approachability, flexibility, and comprehensive functionality. By lowering the barrier to entry for deep learning, Keras has enabled a broader audience to innovate and contribute to the field, accelerating the development and application of AI technologies. Whether for academic research, industry applications, or hobbyist projects, Keras continues to be a leading choice for building and experimenting with neural networks.

    Kind regards Schneppat AI & GPT 5 & SERP

    "The AI Chronicles" Podcast
    en-usMarch 02, 2024
    python
    deep-learning
    tensorflow
    computer-vision
    machine-learning

    Parametrisch ontwerpen versnelt bouwproces

    Parametrisch ontwerpen versnelt bouwproces

    Het ontwerpen van een gebouw duurt soms nog langer dan het bouwen zelf. Het is een lineair proces met veel handwerk. Parametrisch ontwerpen is een nieuwe, innovatieve methode waarmee het ontwerpproces enorm versneld wordt. De ontwikkeltijd van een gebouw wordt er mee teruggebracht van anderhalf jaar tot een maand, stelt CEO Jasper Spiegeler van OMRT.

     

    Het woningtekort in Nederland ligt op dit moment rond de 400.000 woningen, volgens Leontien de Waal, sector banker Bouw bij ABN AMRO. Dat is hoger dan het ooit is geweest, en het is de afgelopen jaren alleen maar toegenomen.

     

    Met hun gasten bespreken Harry Dijkema en Julia Krauwer de mogelijkheden die parametrisch ontwerpen biedt voor het veel sneller ontwerpen van huizen en andere gebouwen.

     

    In deze aflevering hoor je…

    … hoe OMRT van plan is het woningtekort op te lossen

    … hoe parametrisch ontwerp steeds meer wordt omarmd in de bouw- en vastgoedketen

    … hoe het zit met de 'digitale ongeletterdheid'

    … waarom SaaS geen geschikt bedrijfsmodel is voor OMRT

    … hoe parametrisch ook de design- en modewereld verovert

    … wat een 'well-building' is

    … wat de rol van generatieve AI in architectuur kan zijn

    … hoe het gesteld is met circulariteit in de bouw

     

    Links:

    De Innovatiegolf
    nl-nlNovember 30, 2023
    ai
    innovation
    tech
    software
    real estate

    55. O Machine-Learningu i rozwiązaniach Data-Driven dla bankowości z Piotrem Gawrysiakiem

    55. O Machine-Learningu i rozwiązaniach Data-Driven dla bankowości z Piotrem Gawrysiakiem

    Często uciekamy od danych i analizujemy zachowania w procesach biznesowych, a równie często to właśnie dane są podstawą do budowy zaawansowanych systemów IT. Zanim dotkniemy gwarantujących spójność agregatów, nasze operacje przechodzą przez systemy oparte o sztuczną inteligencję czy uczenie maszynowe i to właśnie tym zagadnieniom dziś się przyjrzyjmy.

    Zapraszam dziś na odcinek z wielu powodów dla mnie szczególny, ponieważ moim gościem jest Piotr Gawrysiak, Chief Data Scientist w mBanku i profesor Politechniki Warszawskiej, osoba o ogromnej wiedzy w tematach AI/ML, a także Process Miningu. Po 30 latach życie napisało tu piękną klamrę, bo choć dziś będziemy wspólnie rozmawiać o projektowaniu rozwiązań data-driven czy automatycznej analizie procesów biznesowych, to dawniej chłonąłem treści tworzone przez Piotra pod szyldem magazynów Bajtek i Top Secret... Piotr uchyli rąbka tajemnicy i pokaże jak kierowany przez niego zespół wspiera mBank na polu analizy danych i projektów ML.

    W tym odcinku rozmawiamy m.in. o:

    • projektach opartych w ML/AI w banku,
    • rodzajach problemów możliwych do rozwiązania z użyciem Machine Learningu,
    • procesie tworzenia rozwiązań data-driven,
    • wykorzystywanych technologiach i potrzebnych umiejętnościach w kwestii data-science,
    • późniejszym wdrażaniu przygotowanych modeli i podejścia do dzielenia się danymi,
    • process miningu i automatycznej analizy procesów,
    • wpływie modeli typu ChatGPT-3 na pracę developerów.

    Zapraszam na odcinek!

    Materiały dodatkowe

    • IT w mBanku, więcej rozmów z ekspertami i tematy dookoła software-house'u IT
    • Kursy Andrew NG, kilka kursów od Andrew NG w specjalizacjach Machine Learning, MLOps i Deep Learning
    • Process Mining Warsaw, grupa meetupowa poświęcona tematyce optymalizacji procesów z użyciem Proces Miningu
    • Biblioteka pm4py, implementacja algorytmów Process Mining w Pythonie

    Odcinek powstał we współpracy z mBankiem.

    Better Software Design
    plMarch 21, 2023
    artificial intelligence
    ml
    data-driven
    process mining
    sztuczna inteligencja

    Questioning MLOps with Lak Lakshmanan

    Questioning MLOps with Lak Lakshmanan
    This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously. Lak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn't appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed. Special Guest: Lak Lakshmanan.
    Pipeline Conversations
    en-usOctober 27, 2022
    ai
    infrastructure
    scale
    mlops
    artificial-intelligence

    Questioning MLOps with Lak Lakshmanan

    Questioning MLOps with Lak Lakshmanan
    This week I spoke with Lak Lakhshmanan, who worked for years at Google on ML and AI projects and products at a senior level and he also brings years of experience working on meteorology and other scientific projects previously. Lak brings a ton of experience to the table and it was interesting to hear his suggestions around when it is and isn't appropriate to bring the full set of MLOps tools to the table, for example. We also discussed the fundamentals of doing ML-backed projects as well as the teams needed to make those projects succeed. Special Guest: Lak Lakshmanan.
    Pipeline Conversations
    en-usOctober 27, 2022
    ai
    infrastructure
    scale
    mlops
    artificial-intelligence

    The Full Stack with Charles Frye

    The Full Stack with Charles Frye
    This week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he's part of the core team working on the Full Stack Deep Learning course. Naturally, we get into education for practitioners as well as the things that Charles has seen in his own prior background working on production use cases. We also discuss the ways that tooling to support education as well as productive machine learning can and is being improved. Special Guest: Charles Frye.
    Pipeline Conversations
    en-usOctober 12, 2022
    ai
    education
    mlops
    deep-learning
    artificial-intelligence

    The Full Stack with Charles Frye

    The Full Stack with Charles Frye
    This week I spoke with Charles Frye. Not only has Charles volunteered to be a judge on our Month of MLOps competition happening right now, he's part of the core team working on the Full Stack Deep Learning course. Naturally, we get into education for practitioners as well as the things that Charles has seen in his own prior background working on production use cases. We also discuss the ways that tooling to support education as well as productive machine learning can and is being improved. Special Guest: Charles Frye.
    Pipeline Conversations
    en-usOctober 12, 2022
    ai
    education
    mlops
    deep-learning
    artificial-intelligence

    #223 Adi Polak continuous learner in the big-data space

    #223 Adi Polak continuous learner in the big-data space

    Adi took us on a journey of following learning opportunities. From a machine learning research project in college to learning about distributed systems and big data. We explored how she approached joining Microsoft and creating a scalable network of colleagues while continuing to share her knowledge and learning in the open.

    Here are the links from the show:

    Credits

    Support the show
    Software Developers Journey
    enOctober 11, 2022
    microsoft
    treeverse
    big-data
    machine-learning
    adi polak

    Educating the next generation with Goku Mohandas

    Educating the next generation with Goku Mohandas
    In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML (https://madewithml.com/). Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more. In this conversation we continued to unpack the theme of education in ML, the challenges when it comes to working across the full stack of ML applications, and what he's seen work in his experience working on MadeWithML (https://madewithml.com/). We also discuss some of the patterns he's seen in the production stacks he's seen in his experience consulting with various ML teams as well as where he sees room for improvement in the abstractions that we all rely on to do our work. Goku has generously agreed to be an external judge for our Month of MLOps competition that starts on October 10. If you haven't signed up yet, or want to learn more, please visit zenml.io/competition (https://zenml.io/competition). Special Guest: Goku Mohandas.
    Pipeline Conversations
    en-usSeptember 29, 2022
    ai
    education
    infrastructure
    medicine
    mlops

    Educating the next generation with Goku Mohandas

    Educating the next generation with Goku Mohandas
    In today's conversation, I'm speaking with Goku Mohandas, founder and creator of the amazing online resource MadeWithML (https://madewithml.com/). Goku has a bunch of practical experience, from working with Apple to a startup in the oncology space and much more. In this conversation we continued to unpack the theme of education in ML, the challenges when it comes to working across the full stack of ML applications, and what he's seen work in his experience working on MadeWithML (https://madewithml.com/). We also discuss some of the patterns he's seen in the production stacks he's seen in his experience consulting with various ML teams as well as where he sees room for improvement in the abstractions that we all rely on to do our work. Goku has generously agreed to be an external judge for our Month of MLOps competition that starts on October 10. If you haven't signed up yet, or want to learn more, please visit zenml.io/competition (https://zenml.io/competition). Special Guest: Goku Mohandas.
    Pipeline Conversations
    en-usSeptember 29, 2022
    ai
    education
    infrastructure
    medicine
    mlops

    Data-centric Computer Vision with Eric Landau

    Data-centric Computer Vision with Eric Landau
    This week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren't an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain. We also discuss the much-used term 'data-centric AI' and consider where it's useful and where perhaps there's a little bit of hype. We also get into some of the technical tradeoffs and decisions that come when building a platform. I'm really excited to get to present this episode to you today as I really enjoyed the discussion. Special Guest: Eric Landau.
    Pipeline Conversations
    en-usSeptember 15, 2022
    engineering
    computer-vision
    annotation
    machine-learning
    data-centric-ai

    Data-centric Computer Vision with Eric Landau

    Data-centric Computer Vision with Eric Landau
    This week I spoke with Eric Landau, co-founder of Encord, a platform for data-centric computer vision. This podcast contains a lot of geekery about annotation, and even though Encord aren't an annotation tool per se, Eric and his team have tackled a bunch of quite complicated problems relating to that domain. We also discuss the much-used term 'data-centric AI' and consider where it's useful and where perhaps there's a little bit of hype. We also get into some of the technical tradeoffs and decisions that come when building a platform. I'm really excited to get to present this episode to you today as I really enjoyed the discussion. Special Guest: Eric Landau.
    Pipeline Conversations
    en-usSeptember 15, 2022
    engineering
    computer-vision
    annotation
    machine-learning
    data-centric-ai

    ML Abstractions with Phil Howes

    ML Abstractions with Phil Howes
    This week we dive into the abstractions that we're all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly. We got into some of the big challenges he had working to build out the platform, as well as the core issue of iteration speed that motivates why they're building BaseTen. Phil has experienced quite a few of the industry's end-to-end patterns in the years that he's been working on machine learning and it was great to have that context inform the conversation, too. Special Guest: Phil Howes.
    Pipeline Conversations
    en-usSeptember 05, 2022
    ai
    platforms
    tools
    pipelines
    mlops

    ML Abstractions with Phil Howes

    ML Abstractions with Phil Howes
    This week we dive into the abstractions that we're all trying to layer on top of the core ML processes and workflows. I spoke with Phil Howes, co-founder and chief scientist at BaseTen. BaseTen is a platform that allows data scientists to go from an initial model to an MVP web app quickly. We got into some of the big challenges he had working to build out the platform, as well as the core issue of iteration speed that motivates why they're building BaseTen. Phil has experienced quite a few of the industry's end-to-end patterns in the years that he's been working on machine learning and it was great to have that context inform the conversation, too. Special Guest: Phil Howes.
    Pipeline Conversations
    en-usSeptember 05, 2022
    ai
    platforms
    tools
    pipelines
    mlops

    Building MLOps Tools with Outerbounds

    Building MLOps Tools with Outerbounds
    This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps. Above all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space. Special Guests: Hugo Bowne-Anderson and Savin Goyal.
    Pipeline Conversations
    en-usAugust 22, 2022
    ai
    tools
    pipelines
    mlops
    infrastructure

    Building MLOps Tools with Outerbounds

    Building MLOps Tools with Outerbounds
    This week I spoke with Savin Goyal and Hugo Bowne-Anderson from Outerbounds. They both work on leading, building and helping people put models into production through Metaflow, and I'm sure current users of ZenML will find this conversation interesting to hear how they think through the broader questions and engineering problems involved with MLOps. Above all, we spoke about the challenges involved in building a tool that handles the whole machine learning story, from collecting data to training models, to deployment and back again. In many ways it's great that there are lots of smart people thinking about this really hard problem, and even though it is by no means 'solved' conversations like this make me feel cautiously optimistic about the space. Special Guests: Hugo Bowne-Anderson and Savin Goyal.
    Pipeline Conversations
    en-usAugust 22, 2022
    ai
    tools
    pipelines
    mlops
    infrastructure

    Safe and Testable Computer Vision with Lakera

    Safe and Testable Computer Vision with Lakera
    This week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of Lakera (https://www.lakera.ai/) and Matthias Kraft, also a co-founder and the CPO there. Lakera (https://www.lakera.ai/) is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models. We discuss how they think about the testing of machine learning models, and about how having this safety element upfront has implications for how you go about the testing and ensuring robustness. We specifically dive into how to go about testing computer vision models and the various pitfalls that are to be found in that domain. Special Guests: Mateo Rojas-Carulla and Matthias Kraft.
    Pipeline Conversations
    en-usAugust 04, 2022
    data
    safety
    testing
    monitoring
    mlops

    Safe and Testable Computer Vision with Lakera

    Safe and Testable Computer Vision with Lakera
    This week I spoke with Mateo Rojas-Carulla, the CTO and a co-founder of Lakera (https://www.lakera.ai/) and Matthias Kraft, also a co-founder and the CPO there. Lakera (https://www.lakera.ai/) is an AI safety company that does a lot of work in the computer vision domain, building a platform and tools for users to gain more confidence in the output and functionality of their models. We discuss how they think about the testing of machine learning models, and about how having this safety element upfront has implications for how you go about the testing and ensuring robustness. We specifically dive into how to go about testing computer vision models and the various pitfalls that are to be found in that domain. Special Guests: Mateo Rojas-Carulla and Matthias Kraft.
    Pipeline Conversations
    en-usAugust 04, 2022
    data
    safety
    testing
    monitoring
    mlops

    Autonomous Shipping with Captain AI

    Autonomous Shipping with Captain AI
    This week on the podcast I spoke with Gerard Kruisheer, the CTO and co-founder of Captain AI (https://www.captainai.com/), a company based in the Netherlands working on autonomous shipping out of the busy Rotterdam port. We discussed the unique problems that come with building autonomous vehicles, the extent to which the latest and greatest research informs their work, their production stack and how they handle deployment for their particular setup. As always please let us know if you have guests you'd like me to speak to by sending a message to us on slack or by emailing podcast@zenml.io (podcast@zenml.io). Special Guest: Gerard Kruisheer.
    Pipeline Conversations
    en-usJuly 21, 2022
    shipping
    vehicles
    computer-vision
    autonomous
    machine-learning

    Autonomous Shipping with Captain AI

    Autonomous Shipping with Captain AI
    This week on the podcast I spoke with Gerard Kruisheer, the CTO and co-founder of Captain AI (https://www.captainai.com/), a company based in the Netherlands working on autonomous shipping out of the busy Rotterdam port. We discussed the unique problems that come with building autonomous vehicles, the extent to which the latest and greatest research informs their work, their production stack and how they handle deployment for their particular setup. As always please let us know if you have guests you'd like me to speak to by sending a message to us on slack or by emailing podcast@zenml.io (podcast@zenml.io). Special Guest: Gerard Kruisheer.
    Pipeline Conversations
    en-usJuly 21, 2022
    shipping
    vehicles
    computer-vision
    autonomous
    machine-learning
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