{"podcast":{"title":"The Data Science Podcast with Fexingo: Analytics, Machine Learning, and Data-Driven Conversations","slug":"the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","podcast_index_feed_id":7871831,"rss_url":"https://feeds.fexingo.com/business/the-data-science-podcast.xml","website_url":"https://www.fexingo.com/","image_url":"https://audio.fexingo.com/business/the-data-science-podcast/cover.png","author":"Fexingo","episode_count":118,"summary":"Lucas and Luna sit at a data-science workstation, two thin laptops open to scatter plots and clustering visualizations, and ask: what can we actually learn from the numbers? Each episode of The Data Science Podcast with Fexingo is a grounded, specific conversation about a single analytics problem or machine-learning method — from regularization in regression to the bias-variance trade-off in random forests. Lucas leads with a journalistic eye for how models are built and tested in the real world, citing actual case studies like how Netflix used matrix factorization for recommendations or how healthcare researchers apply survival analysis to clinical trials. Luna keeps the discussion honest, asking about data quality, feature engineering pitfalls, and whether a model’s accuracy actually translates to business value. They never resort to buzzwords: instead, they walk through the workflow from data collection to deployment, discussing trade-offs like interpretability versus performance. The show serves data scientists, analysts, and engineers who want to stay sharp on methods without the hype. Listeners walk away with a clearer understanding of why one algorithm beats another on a gi…","last_synced_at":"2026-07-19T08:17:23.323447+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831"},"episode":{"title":"How Data Scientists Use Transfer Learning to Solve Cold Start Problems","slug":"how-data-scientists-use-transfer-learning-to-solve-cold-start-problems","published_at":"2026-06-17T08:17:13+00:00","page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-transfer-learning-to-solve-cold-start-problems","show_page_url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831","url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0056.mp3","audio_url":"https://audio.fexingo.com/business/the-data-science-podcast/episode-0056.mp3","summary":"When a new product launches with zero user history, recommendation systems and personalization engines face the 'cold start' problem — they have no data to learn from. In this episode, Lucas and Luna explore how data scientists are using transfer learning to jump-start predictions without waiting for users to generate behavior. They walk through a real example from an e-commerce startup that used a pre-trained model from a similar product category to generate initial recommendations, cutting the ramp-up time from six weeks to under three days. The hosts discuss the key trade-offs: when transfer works, when it can backfire, and how to fine-tune effectively. They also touch on the difference between transfer learning and multi-task learning, and why this technique is becoming a standard tool in the modern data science toolkit. If you've ever wondered how a brand-new app seems to know what you like on day one, this episode explains the data science behind it. #TransferLearning #ColdStartProblem #RecommendationSystems #MachineLearning #DataScience #FineTuning #PreTrainedModels #ECommerceData #Personalization #FeatureExtraction #DomainAdaptation #FewShotLearning #ZeroShotLearning #ModelDeployment #StartupAnalytics #DeepLearning #Technology #FexingoBusiness Keep every episode free: buymeacoffee.com/fexingo","meta_description":"When a new product launches with zero user history, recommendation systems and personalization engines face the 'cold start' problem — they have no data t…","key_points":[],"chapters":[],"topics":[],"duration_seconds":803,"processing_state":"not_requested","actions":[{"name":"request_transcript","method":"POST","url":"https://stenobird.com/v1/public/podcasts/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/episodes/how-data-scientists-use-transfer-learning-to-solve-cold-start-problems/transcription-requests","description":"Idempotently request low-priority transcript generation for this episode."},{"name":"read_markdown","method":"GET","url":"https://stenobird.com/podcast/the-data-science-podcast-with-fexingo-analytics-machine-learning-and-data-driven-conversations-7871831/how-data-scientists-use-transfer-learning-to-solve-cold-start-problems.md","description":"Read the agent-friendly Markdown representation of this episode resource."}]}}