| Nov. 2021 |
Activeloop is the Database for AI company |
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| Jan. 2022 |
Deep dive on connecting hub datasets to ML frameworks |
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| Jan. 2022 |
Storage providers and cache chains |
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| Jan. 2022 |
Deeper dive on dynamic tensors which power hub data streaming |
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| Jan. 2022 |
Deep dive on parallel computing with hub |
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| Feb. 2022 |
Community meetup: Deep Lake and Activeloop Platform Updates |
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| Oct. 2022 |
Deep Lake and Weights & Biases: How to use Deep Lake queries to create optimal datasets for training |
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| Oct. 2022 |
Activeloop and Weights & Biases: Understanding the W&B artifact for the created Deep Lake dataset |
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| Oct. 2022 |
Deep Lake and Weights & Biases: Examining the training logs in W&B |
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| Oct. 2022 |
What is Deep Lake? Activeloop Deep Lake Product Demo |
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| Nov. 2022 |
How to Use Activeloop Deep Lake Visualizer? Official Tutorial |
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| Jul. 2023 |
The 5 Layers of Foundational Models: LangChain & Vector Databases in Production Course |
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| Dec. 2023 |
Build a Outfit Recommender App with Retrieval Augmented Generation and Deep Lake and GPT-4 Vision |
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| Jan. 2024 |
Building Fullstack Applications with LlamaIndex: LlamaPacks & more |
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| Jan. 2024 |
Next-Level Automation: Data Agents In LlamaIndex |
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| Jan. 2024 |
Advanced Retrieval Strategies: Deep Memory, Small to Big Retrieval, Structured and Unstructured Data |
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| Jan. 2024 |
Overview of Retrieval Augmented Generation and Its Components |
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| Jan. 2024 |
Challenges with Naive RAG & How to Evaluate RAG Applications? |
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| Jan. 2024 |
Introduction to LlamaIndex by Jerry Liu |
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