Bayesian Modeling and Probabilistic Programming - Rob Zinkov
01/22/24 • 54 min
We talked about:
- Rob’s background
- Going from software engineering to Bayesian modeling
- Frequentist vs Bayesian modeling approach
- About integrals
- Probabilistic programming and samplers
- MCMC and Hakaru
- Language vs library
- Encoding dependencies and relationships into a model
- Stan, HMC (Hamiltonian Monte Carlo) , and NUTS
- Sources for learning about Bayesian modeling
- Reaching out to Rob
Links:
- Book 1: https://bayesiancomputationbook.com/welcome.html
- Book/Course: https://xcelab.net/rm/statistical-rethinking/
Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html
We talked about:
- Rob’s background
- Going from software engineering to Bayesian modeling
- Frequentist vs Bayesian modeling approach
- About integrals
- Probabilistic programming and samplers
- MCMC and Hakaru
- Language vs library
- Encoding dependencies and relationships into a model
- Stan, HMC (Hamiltonian Monte Carlo) , and NUTS
- Sources for learning about Bayesian modeling
- Reaching out to Rob
Links:
- Book 1: https://bayesiancomputationbook.com/welcome.html
- Book/Course: https://xcelab.net/rm/statistical-rethinking/
Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html
Previous Episode
Navigating Challenges and Innovations in Search Technologies - Atita Arora
We talked about:
- Atita’s background
- How NLP relates to search
- Atita’s experience with Lucidworks and OpenSource Connections
- Atita’s experience with Qdrant and vector databases
- Utilizing vector search
- Major changes to search Atita has noticed throughout her career
- RAG (Retrieval-Augmented Generation)
- Building a chatbot out of transcripts with LLMs
- Ingesting the data and evaluating the results
- Keeping humans in the loop
- Application of vector databases for machine learning
- Collaborative filtering
- Atita’s resource recommendations
Links:
- LinkedIn: https://www.linkedin.com/in/atitaarora/
- Twitter: https://x.com/atitaarora
- Github: https://github.com/atarora
- Human-in-the-Loop Machine Learning: https://www.manning.com/books/human-in-the-loop-machine-learning
- Relevant Search: https://www.manning.com/books/relevant-search
- Let's learn about Vectors: https://hub.superlinked.com/ Langchain: https://python.langchain.com/docs/get_started/introduction
- Qdrant blog: https://blog.qdrant.tech/
- OpenSource Connections Blog: https://opensourceconnections.com/blog/
Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html
Next Episode
Stock Market Analysis with Python and Machine Learning - Ivan Brigida
We talked about:
- Ivan’s background
- How Ivan became interested in investing
- Getting financial data to run simulations
- Open, High, Low, Close, Volume
- Risk management strategy
- Testing your trading strategies
- Sticking to your strategy
- Important metrics and remembering about trading fees
- Important features
- Deployment
- How DataTalks.Club courses helped Ivan
- Ivan’s site and course sign-up
Links:
- Exploring Finance APIs: https://pythoninvest.com/long-read/exploring-finance-apis
- Python Invest Blog Articles: https://pythoninvest.com/blog
Free ML Engineering course: http://mlzoomcamp.com Join DataTalks.Club: https://datatalks.club/slack.html Our events: https://datatalks.club/events.html
If you like this episode you’ll love
Episode Comments
Generate a badge
Get a badge for your website that links back to this episode
<a href="https://goodpods.com/podcasts/datatalksclub-172386/bayesian-modeling-and-probabilistic-programming-rob-zinkov-42444453"> <img src="https://storage.googleapis.com/goodpods-images-bucket/badges/generic-badge-1.svg" alt="listen to bayesian modeling and probabilistic programming - rob zinkov on goodpods" style="width: 225px" /> </a>
Copy