Discover how Dr. Travis Oliphant's passion for open source led to the creation of NumPy and SciPy, revolutionizing data science and machine learning. A must-listen episode with your host Jon Krohn! Watch the episode here: https://bit.ly/sds765 Thank you to Women in Analytics (WIA) and Data Universe, for supporting this episode of SuperDataScience, enabling the show to be freely available on all major podcasting platforms and on YouTube. #datascience #numpy #scipy
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Check out today’s new episode featuring Esme Tovar! She works at an awesome place nicknamed The Tech and helps our listeners grasp concepts like data science, computer science, and computational thinking. These topics are part of our daily lives and feel so much simpler after hearing Esme's insights. Listen to the episode here: https://lnkd.in/egSRJ4bh or on your favorite podcast provider. #AIForKids #TechTalk #DataScience #ComputerScience #ComputationalThinking #TheTech #PodcastForKids #LearnWithEsme
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VP of Business Intelligence & Data Science | Professor of Practice | Analytical Alchemist: Transforming Data into Business Gold
🎃 New podcast alert: Data Science Rabbit goes full moon. (Warning: May cause uncontrollable urges to howl at datasets.) Brace yourselves, data nerds. The Data Science Rabbit Podcast just dropped a Halloween special that's more terrifying than your last failed model deployment. 🐺 Episode 1: "When AI Met Werewolf" https://lnkd.in/ex4Hbq4p More scary episodes to come in the next few weeks. Don't say I didn't warn you. Available now on Spotify and all major podcast platforms. Listen at your own risk – side effects may include increased hair growth and an inexplicable desire to chase squirrels while coding. PS: If you turn into a werewolf after listening, please rate us 5 stars. It's the least you could do before devouring the interns. #DataScience #AIWerewolves #PodcastsFromHell
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Stay ahead in the battle against digital deception and understand the potential for misuse within the world of deepfakes. 🎧 BlackBerry’s Senior Vice President of Product Engineering and Data Science, Shiladitya Sircar, joins Daniel Miessler on the Unsupervised Learning podcast: blck.by/3BWYyKo
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🇺🇸🇮🇱 Ph.D. EE | Author | Fractional CTO | LLM, Generative ai, radar signal processing and Machine vision researcher | expert witness | Unapologetically Jewish, American patriot, Zionist and Israeli 🇺🇸 🇮🇱
Our Latest episode is on the Cramer-Rao bound, specifically, Rao’s seminal paper.
Head of AI @ Cyber Stealth | Math PhD | Scientific Content Creator | Lecturer | Podcast Host(40+ podcasts about AI & math) | Deep Learning(DL) & Data Science(DS) Expert | > 350 DL Paper Reviews | 55K followers |
🎙️ New Podcast Episode: The Rao-Cramer Inequality—Where Precision Meets Boundaries 🤯 What if mathematics could tell us the ultimate limit of precision in estimation? 📊 Our latest math-heavy podcast episode dives into the Rao-Cramer inequality, a foundational result in statistics that defines the lower bound for the variance of any unbiased estimator. 🔍 What You’ll Learn: 💠 Origins and Insights: Explore the deep connections between information theory and statistical estimation as established in C.R. Rao’s groundbreaking 1945 paper. 🥘 The Role of Fisher Information: Understand how this concept quantifies the information a dataset carries about its parameters and its impact on estimation precision. 🌏 Real-World Applications: From engineering signal processing to optimizing machine learning algorithms, discover how this inequality shapes decision-making and model design. 🌟 Mathematical Breakdown: We break down the inequality’s elegant formulation, explaining why it’s a benchmark for statistical performance. 💡 Whether you’re a data scientist, statistician, or math enthusiast, this episode will challenge you to think critically about what precision means in the context of uncertainty. 🚇 The episode is available on youtube and spotify. 🎤 Daniel A. Nir Regev #Statistics #MathPodcast #RaoCramerInequality #FisherInformation #DataScience
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Our latest #BITTechTalk podcast episode features an inspiring conversation with Vera Dureke, BIT Chicago President, mathematician, and data scientist. Vera dives into her journey in the tech industry, her role in BIT Chicago, and the evolving field of data science. This episode is a must-listen for anyone interested in tech and data science. 🔗 bit.ly/BITTP-Vera #BlacksInTech #TechPodcast #DataScience #WomenInTech bit.ly/BITTP-Vera
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Creating generative art with R is an incredibly abstract process that blends coding, data, mathematics, and artistic insight in a truly unique way. Experience it firsthand as Meghan Harris, MPH delves into this fascinating topic on the R for the Rest of Us podcast. Check out the episode through the audio and video links in the comments below! #Rstats #GenerativeArt #CreativeCoding #DataVisualization
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📢 NEW Substack is online! I was guest in Jon Krohn’s Super Data Science podcast. In my latest Substack post I share some insightful Q&As from our exciting interview on how to learn Data Engineering! Check out the complete blog post here 👉 https://bit.ly/4ahA62V #dataengineer #dataengineering #datascience #bigdata #learndataengineering
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Head of AI @ Cyber Stealth | Math PhD | Scientific Content Creator | Lecturer | Podcast Host(40+ podcasts about AI & math) | Deep Learning(DL) & Data Science(DS) Expert | > 350 DL Paper Reviews | 55K followers |
🎙️ New Podcast Episode: The Rao-Cramer Inequality—Where Precision Meets Boundaries 🤯 What if mathematics could tell us the ultimate limit of precision in estimation? 📊 Our latest math-heavy podcast episode dives into the Rao-Cramer inequality, a foundational result in statistics that defines the lower bound for the variance of any unbiased estimator. 🔍 What You’ll Learn: 💠 Origins and Insights: Explore the deep connections between information theory and statistical estimation as established in C.R. Rao’s groundbreaking 1945 paper. 🥘 The Role of Fisher Information: Understand how this concept quantifies the information a dataset carries about its parameters and its impact on estimation precision. 🌏 Real-World Applications: From engineering signal processing to optimizing machine learning algorithms, discover how this inequality shapes decision-making and model design. 🌟 Mathematical Breakdown: We break down the inequality’s elegant formulation, explaining why it’s a benchmark for statistical performance. 💡 Whether you’re a data scientist, statistician, or math enthusiast, this episode will challenge you to think critically about what precision means in the context of uncertainty. 🚇 The episode is available on youtube and spotify. 🎤 Daniel A. Nir Regev #Statistics #MathPodcast #RaoCramerInequality #FisherInformation #DataScience
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Proven Builder of Innovative Teams, Products, and Processes | Healthcare AI Leader | Data Equity Advocate
Sometimes, it's nice to get back to the basics and have conversations about the importance of statistics in ML/AI and how these mathematical foundations are vital to monitor algorithms over time--limiting the potential harm they may cause. I enjoyed joining Data Nerds in the OR #podcast, hosted by Bruce Ramshaw, to chat about this and other fun data topics! Listen to the full discussion here: https://lnkd.in/eTAdXFZa #AIinHealthcare #DataScience #PlatypusHealth Platypus Health
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I teach Data Engineering & match talents with dream jobs | 10+ years of experience | 3x LinkedIn Top Voice | 200k YouTube subscribers
📢 NEW Substack is online! I was guest in Jon Krohn’s Super Data Science podcast. In my latest Substack post I share some insightful Q&As from our exciting interview on how to learn Data Engineering! Check out the complete blog post here 👉 https://bit.ly/4ahA62V #dataengineer #dataengineering #datascience #bigdata #learndataengineering
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