By studying the underlying physical dynamics of flying embers, scholars aim to develop AI-driven models to understand and mitigate spot fires, which often lead to home destruction. Learn more about this study supported by HAI:
MACHINE LEARNING
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Advanced Retrieval Methods for RAG Applications
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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 -
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
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Back prop and image credit: https://
youtube.com/watch?v=An5z8l
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… Original post credit: @bindureddy 10/10 -
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
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Neural Network Training: Iteration and Data Requirements
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6. Iteration: We repeat steps 2 to 6 for all the data points in your training set multiple times (epochs). Hence, your neural network is likely to be a better fit, if you have more training data points. 7/10
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Gradient Descent: Adjusting Weights and Biases to Reduce Loss
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5: Gradient Descent: We now adjust the weights and biases based on the gradients calculated in the last step. Typically this is done by multiplying the gradient by a small factor called learning rate. The basic idea is to reduce the error or loss. 6/10
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Backpropagation: Computing Gradients Using the Chain Rule
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You move backwards from the last layer using the chain rule of calculus and compute "gradients". Basically, you are calculating the gradient of the loss function with respect to each weight or bias 5/10