Friday viewing: "Advanced Retrieval" Webinar I had a great time discussing: – Importance of preprocessing @mrobinson0623 of @UnstructuredIO – Different retrieval methods to power RAG applications
– What lies beyond simple RAG? @atroyn of @trychroma
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Advanced Retrieval Methods for RAG Applications
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Change Your Default Bot in Settings
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You can change your default bot from the settings page, below the phone number setting.
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Where Are All the Women in Tech Industry?
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Where are all the #WomeninTech? https://
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Hybrid Cloud AI Strategy Guide for Data Executives
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Unlock hybrid cloud's potential to boost enterprise AI with our white paper on the top AI considerations every Chief Data & Analytics exec should know. Produced by @DominoDataLab
, @nvidia & @ventanaresearch. https://
domino.buzz/3Yhtexb #AI #DataScience #HybridCloud. -
Policymakers Must Examine AI Harms and Automation Risks
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"We urge policymakers to instead draw on solid scholarship that investigates the harms and risks of AI—and the harms caused by delegating authority to automated systems" w/
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The Future Of Product Management: Embracing AI’s Revolution
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The Future Of Product Management: Embracing AI's Revolution
#AI #AIio #BigData #ML #NLU #Futureofwork @chrismessina @ChristopherIsak @davidwkenny @debashis_dutta @petitegeek @fabiomoioli @GaryMarcus @asokan_telecom http://
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Best Generative AI Models: Comparative Evaluation Guide
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Who is the best? Evaluating different types of generative AI models By @ingliguori https://
bit.ly/3pviApD #chatgpt #bard #cloudeai #chatgpt4 #llms #generativeai #AI #artificialintelligence #machinelearning #mlops #modelops -
Gradient Descent and Backpropagation Learning Resources
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To learn more: Gradient descent: https://
youtube.com/watch?v=qg4Pch
TECck
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Back prop and image credit: https://
youtube.com/watch?v=An5z8l
R8asY
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Low Test Error Indicates Successful Model Learning and Generalization
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If your error on the test set is low, you have learned the weights and biases of a model that predict the output given specific inputs and you can now use it to predict unseen data. 9/10
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Evaluation: Testing Neural Networks on Unseen Data Points
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7. Evaluation: You should always keep aside some data points from your original training set for testing. Here we evaluate how the NN predicts data points it's never been trained on. 8/10