Podcast Index
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The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations
Fexingo
BBC Sports News
BBC
Jorge Ramos y su Banda
Jorge Ramos
Fisher Investments - Market Insights
Fisher Investments
Houston Public Media Newscasts
Houston Public Media
Эхо Москвы
Feed Master by Umputun
Soccer Down Here
OSG Sports
The Dallas Cowboys Mike Fisher 'Fish Report'
Fan Stream Sports, Bleav
NASA Daily
ElectraCast Media LLC
Admonition Podcast
Aaron Cozort
長谷川滋利のMLBトーク
Game Plan
WSJ Opinion: Potomac Watch
Paul Gigot, The Wall Street Journal
The Dan O'Donnell Show
Dan O'Donnell (WISNAM)
Deep Values
TheDeepValue
The Bill Arnold Show
Faith Radio
The BradCast w/ Brad Friedman
Brad Friedman
The Employment Law Show
AM640
Great Night Audio Feed
Brian Brushwood & Justin Robert Young
陪你讀聖經
愛的影響力/爱的影响力
The Steve Finnegan Show
Steve Finnegan
The Dan Evans Show
Dan Evans
Daily Lectio Divina
Abiding Way Ministries
Los Bonkers
Carlos, Mayito, Angel, Mario & Chachi
The Perpetual Wealth Podcast
Patrick Donohoe
El Primer Palo
esRadio
Dukes & Bell
Audacy
Time To Pray: Common Worship Day and Night Prayer
The Church of England
The Essential
Will Media - Chiara Piotto
Secretos de LiLi Coach
Streamla
Wild West Tales - Old Time Radio Westerns
theaterofthemind-otr.com
Los Jefes del Deporte con Álvaro Morales
Nueva Network | Qué Onnda
Alex Jones Network Shows Commercial Free - RonGibsonChannel on Odysee
Alex Jones Network Shows Commercial Free - RonGibsonChannel
第一次播音 06:00 - 07:00（北京时间）
RFI - 法国国际广播电台
Boys loving woman. Based on true events
LOVE love! By young offender
Apple News Radio ワンボタンの声
ワンボタンの声制作委員会
Live at 5
1027 The Game
Search For Lawful Government w/ Ron Avery
Search For Lawful Government w/ Ron Avery
All
Fexingo
In this episode, Lucas and Luna dive into the practical applications of Sentence-BERT (SBERT) for semantic search in production. They discuss how SBERT converts text into dense vector embeddings, enabling similarity sear...
Lucas and Luna explore the practical use of SHAP (SHapley Additive exPlanations) for interpreting complex machine learning models. They walk through a real-world example: a credit risk model from a mid-sized European fin...
For Episode 100 of The Data Science Podcast, Lucas and Luna explore the controversial practice of training AI models on synthetic data. With real-world examples from autonomous vehicle companies like Waymo and medical im...
In this episode, Lucas and Luna dive into Temporal Fusion Transformers (TFT), a deep learning architecture that has changed how data scientists approach time series forecasting. They walk through a concrete case from a m...
In this episode, Lucas and Luna dive into how Spotify uses reinforcement learning to personalize playlists like Discover Weekly and Release Radar. They break down the multi-armed bandit problem, explore how Spotify balan...
Episode 97 dives into counterfactual explanations — the 'what if' tools helping data scientists debug models and build stakeholder trust. Lucas and Luna walk through a concrete example: a credit-approval model that rejec...
In this episode, Lucas and Luna dive into multimodal zero-shot learning, the technique that lets AI models like CLIP recognize objects, scenes, and text across images without ever being explicitly trained on those combin...
In Episode 95 of The Data Science Podcast with Fexingo, Lucas and Luna dive into a practical yet underappreciated technique: using k-nearest neighbors for anomaly detection. They kick off with a real-world story from a m...
In episode 94 of The Data Science Podcast with Fexingo, Lucas and Luna explore how active learning cuts labeling costs by 80 percent while maintaining model accuracy. Using a concrete example from a medical imaging start...
Episode 93 of The Data Science Podcast with Fexingo dives into Thompson Sampling, a Bayesian approach to online experimentation that balances exploration and exploitation better than traditional A/B testing. Lucas and Lu...
Episode 92 of The Data Science Podcast with Fexingo dives into embedded analytics—the practice of integrating dashboards, reports, and AI insights directly into customer-facing products. Lucas and Luna explore how compan...
Most marketing attribution models are correlational — they tell you what happened, not why. In this episode, Lucas and Luna break down how data scientists are using causal inference techniques, specifically double machin...
In this episode, Lucas and Luna explore how knowledge graphs are supercharging retrieval-augmented generation (RAG) systems. They break down a concrete example: how a financial services firm used a knowledge graph built ...
Lucas and Luna explore how graph neural networks are transforming recommendation systems, using the example of Pinterest's PinSage model. They break down how GNNs capture relational data like user-item interactions to ge...
Episode 88 of The Data Science Podcast dives into dimensionality reduction — but not for preprocessing. Lucas and Luna explore how data scientists use t-SNE and UMAP to visualize high-dimensional data, from customer segm...
In episode 87 of The Data Science Podcast, Lucas and Luna explore manifold learning—a powerful technique for dimensionality reduction that goes beyond PCA. They focus on t-SNE and UMAP, explaining how these algorithms pr...
In this episode, Lucas and Luna explore the concept of the Pareto frontier, a powerful framework for multi-objective optimization in data science. Starting with a concrete example—a ride-hailing company balancing driver ...
Lucas and Luna dive into Neural Radiance Fields (NeRFs), a technique that has reshaped 3D reconstruction from 2D images. They walk through how NeRFs work at a high level—converting sparse photographs into continuous volu...
In this episode of The Data Science Podcast, Lucas and Luna explore how data scientists are using diffusion models — the technology behind tools like DALL-E and Stable Diffusion — for image generation. They break down th...
In this episode, Lucas and Luna explore how data scientists apply transfer learning to solve image classification problems with very little labeled data. They break down the concrete steps: taking a pre-trained model lik...
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