It's easy to train a Model on your Data. Just a couple of clicks 1. Under What do you want to do? select Train a model.
2. Name your model.
3. Under Label select species. Select the target column and click Train. That's how simple it is to train a Model. 4/6
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Simple Model Training in Just a Few Clicks
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Machine Learning Model: Key Capabilities and Workflow Steps
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What are all things you can do? – Predict Missing Values
– Spot Abnormal Values
– Train and Evaluate the Model
– Analyze and Interpret the Model Results
– Exporting the trained Model 3/6 -
Simple ML for Sheets: Building ML Models in Google Sheets
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No Google Colab, No Jupyter Notebook! Introducing Simple ML for Sheets. Let's build Machine Learning Models right inside Google Sheets. Thread
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Video Processing with OpenCV: Frame Handling and Optimization
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🎥 New Video Alert 🎥https://t.co/KDcahmo8VG
— Satya Mallick (@LearnOpenCV) 12 février 2024
Check out our latest video as we explore the realm of video processing using OpenCV. Understand the nuances of handling video frames, managing errors, and optimizing video operations with practical, step-by-step instructions.
Go… pic.twitter.com/rmNVbyUbl3New Alert https://
youtube.com/watch?v=kt2ygR
lngN8
… Check out our latest video as we explore the realm of video processing using OpenCV. Understand the nuances of handling video frames, managing errors, and optimizing video operations with practical, step-by-step instructions.
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Machine Learning Engineering Open Book: Complete DL Model Guide
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"Machine Learning Engineering Open Book" by Stas Bekman This open book has a lot of information on the Engineering aspects of building DL / ML models specifically LLM and Multi-modal models. This open book is continuously being updated. A pdf version can also be downloaded.
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Programming Productivity: Balancing Planning with Discovery
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one trick to programming productivity is figure out half of the structure of your program by thinking hard, and then discover the other half from the process of trying to actually write the code for what you've been picturing
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ML Abstraction Design: Balancing Current Practice and Future Innovation
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ML abstraction design is hard in part because progress is made by people breaking through the existing abstractions. So must write code that facilitates the way things are done today while also making it easy to try out new ideas of how things could be done tomorrow.
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RAGAS v0.1 Released for RAG Application Evaluation
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Evaluate RAG applications with RAGAS v0.1 RAGAS is one of the most popular OSS ways to test RAG applications And they just released v0.1! If you've been meaning to improve your evaluation pipeline for RAG applications – never a better time to start! https://
github.com/explodinggradi
ents/ragas
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Deploying PDF RAG Chatbots to Production with LangServe
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Unlock the Power of Lang Chain: Deploying to Production Made Easy In part 3 of his series, @austinbv shows how to deploy a PDF RAG chatbot to production using LangServe YouTube: https://
youtube.com/watch?v=CbBIwV
xjdP8
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Connecting Chatbots to External Data Sources via RAG
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Learn how to connect chatbots to external data sources via RAG: https://
txt.cohere.com/exploring-chat
-rag/
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