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  • Deep Lake Docs
  • Vector Store Quickstart
  • Deep Learning Quickstart
  • Storage & Credentialschevron-right
  • List of ML Datasetsarrow-up-right-from-square
  • 🏢High-Performance Features
    • Introduction
    • Performant Dataloader
    • Tensor Query Language (TQL)chevron-right
    • Index for ANN Search
    • Managed Tensor Databasechevron-right
  • 📚EXAMPLE CODE
  • Getting Startedchevron-right
    • Vector Storechevron-right
    • Deep Learningchevron-right
      • Step 1: Hello World
      • Step 2: Creating Deep Lake Datasets
      • Step 3: Understanding Compression
      • Step 4: Accessing and Updating Data
      • Step 5: Visualizing Datasets
      • Step 6: Using Activeloop Storage
      • Step 7: Connecting Deep Lake Datasets to ML Frameworks
      • Step 8: Parallel Computing
      • Step 9: Dataset Version Control
      • Step 10: Dataset Filtering
  • Tutorials (w Colab)chevron-right
  • Playbookschevron-right
  • Low-Level API Summary
  • 🔬Technical Details
    • Best Practiceschevron-right
    • Data Layout
    • Version Control and Querying
    • Dataset Visualization
    • Tensor Relationships
    • Visualizer Integration
    • Shuffling in dataloaders
    • How to Contribute
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  1. Getting Started

Deep Learning

The comprehensive guide for Deep Lake in Deep Learning applications.

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This Deep Learning Getting Started guide is available as a Colab Notebookarrow-up-right

Step 1: Hello Worldchevron-rightStep 2: Creating Deep Lake Datasetschevron-rightStep 3: Understanding Compressionchevron-rightStep 4: Accessing and Updating Datachevron-rightStep 5: Visualizing Datasetschevron-rightStep 6: Using Activeloop Storagechevron-rightStep 7: Connecting Deep Lake Datasets to ML Frameworkschevron-rightStep 8: Parallel Computingchevron-rightStep 9: Dataset Version Controlchevron-rightStep 10: Dataset Filteringchevron-right

PreviousStep 4: Customizing Vector Storeschevron-leftNextStep 1: Hello Worldchevron-right

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