Generative #AI Application Integration Patterns — Integrate large language models into your applications: https://
amzn.to/3ZxirkU v/ @PacktPublishing ———
#LLMs #DataScience #ML #GenAI #GenerativeAI #DeepLearning #MachineLearning #DataScientists
———
Key Features:
Get familiar
MACHINE LEARNING
-
Generative AI Application Integration Patterns for LLMs
By
–
-
Machine Learning and Generative AI for Marketing with Python
By
–
#MachineLearning and #GenerativeAI for Marketing using #Python :: http://
amzn.to/4dNES9Z v/ @PacktPublishing ——————
#AI #DataScience #ML #GenAI #Martech #CMO #Adtech -
Machine Learning Techniques for Handling Imbalanced Data
By
–
#MachineLearning and #DeepLearning Techniques for Handling Imbalanced Data: http://
amzn.to/3Wuch3C v/ @PacktPublishing Purchase of print or Kindle book includes free PDF eBook copy.
—
#DataScience #DataScientist #AI #Statistics #ML #DataLiteracy
—
What you will learn:
Use -
Data Management Critical to Building Successful AI Products
By
–
"AI models are very very smart. And also, insanely dumb."@ShaunMClowes on why building great AI products is 90% a data-management problem. https://t.co/kWMQtFDglA pic.twitter.com/DA9whOJuL0
— Lenny Rachitsky (@lennysan) 30 décembre 2024"AI models are very very smart. And also, insanely dumb." @ShaunMClowes on why building great AI products is 90% a data-management problem.
-
Five Years of AI: Key Technical Lessons in Language Models
By
–
Reflecting back, these were the biggest technical lessons for me in AI in the past five years: 2020: you can cast any language task as sequence prediction and learn it via pretrain + finetune 2021: scaling to GPT-3 size enables doing arbitrary tasks specified via instructions
-
Artificial Intelligence’s Critical Role in Modern Daily Life
By
–
Artificial intelligence: how it is already important in everyday life
#AI #AIio #BigData #ML #NLU #Futureofwork @TopCyberNews @SpirosMargaris @MarshaCollier @MHiesboeck
@MHcommunicate @Fisher85M @MikeQuindazzi @NealSchaffer -
Positive and Negative Samples in Contrastive Learning
By
–
Not sure about the context, but it probably refers the negative sample in a contrastive learning setting? The positive sample is similar to query, the negative sample is different from the query.
-
Scarcity of Multi-Turn Q&A Datasets in Machine Learning
By
–
I am actually not sure, that's a good point actually. In that sense, there also aren't that many multi-turn Q&A datasets out there (there are some, but maybe only 5% of all Q&A datasets are multi-turn)
-
Gratitude for Help with ML and AI Learning Journey
By
–
Thanks, and it's super nice to hear that it helped getting into ML & AI!!
-
Meta Research on Generative Retrieval for Recommendation Systems
By
–
Newly published research for generative retrieval for recommendations from teams at Meta. – Preference Discerning with LLM-Enhanced Generative Retrieval https://
go.fb.me/evvcu8
– Unifying Generative and Dense Retrieval for Sequential Recommendation https://
go.fb.me/i7l955