AudioCraft by Meta AI is a one-stop codebase for generative audio consisting of three models: MusicGen, AudioGen & EnCodec — supporting both compression + generation of high-quality music and sound effects from text.. Get the code
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Parent Document Retriever Now Available JavaScript Python
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We release parent document retriever in Python earlier this week, now available in JS Blends "small chunk size for embedding to capture semantic meaning" with "large chunk returned, to provide more context" OG tweet: https://
x.com/hwchase17/stat
us/1689301894769094656?s=20
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Keras Core Beta Achieves 100K Downloads and 136 Community Pull Requests
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We launched the Keras Core beta one month ago. Since then, the package was downloaded nearly 100,000 times and we received 136 PRs from the community. We've also made fast progress with the codebase in a short time. We're on track for the big release in a couple of months.
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LangSmith Newsletter Second Edition Highlights Community Growth
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We just sent out the second edition of our newsletter, and its fun to see the pace of the community We added two new sections ("Use-cases we love" and "Thank You's") to highlight this We also cover LangSmith updates like team support and our new cookbook repo
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Build Custom FAQ Chatbot with BERT Technology
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Build Custom FAQ Chatbot with BERT : Chatbots have become increasingly standard and valuable interfaces employed by numerous organizations for… #DataAnalytics #DataScience #DataDriven #DeepLearning #BusinessIntelligence #SaaS #Blockchain #ITManager https://
analyticsvidhya.com/blog/2023/07/b
uild-custom-faq-chatbot-with-bert/?utm_source=dlvr.it&utm_medium=twitter
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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 -
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
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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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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