> For the complete documentation index, see [llms.txt](https://docs-v3.activeloop.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs-v3.activeloop.ai/examples.md).

# Examples

- [Deep Learning](https://docs-v3.activeloop.ai/examples/dl.md): Using Deep Lake for managing data in Deep Learning applications.
- [Deep Learning Quickstart](https://docs-v3.activeloop.ai/examples/dl/quickstart.md): A jump-start guide to using Deep Lake for Deep Learning.
- [Deep Learning Guide](https://docs-v3.activeloop.ai/examples/dl/guide.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](https://docs-v3.activeloop.ai/examples/dl/guide/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](https://docs-v3.activeloop.ai/examples/dl/guide/creating-datasets.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](https://docs-v3.activeloop.ai/examples/dl/guide/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](https://docs-v3.activeloop.ai/examples/dl/guide/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](https://docs-v3.activeloop.ai/examples/dl/guide/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](https://docs-v3.activeloop.ai/examples/dl/guide/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](https://docs-v3.activeloop.ai/examples/dl/guide/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](https://docs-v3.activeloop.ai/examples/dl/guide/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](https://docs-v3.activeloop.ai/examples/dl/guide/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](https://docs-v3.activeloop.ai/examples/dl/guide/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Deep Learning Tutorials](https://docs-v3.activeloop.ai/examples/dl/tutorials.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-complex-datasets.md): Converting a multi-annotation dataset to Deep Lake format is helpful for understanding how to use Deep Lake with rich data.
- [Creating Object Detection Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-object-detection-datasets.md): Converting an object detection dataset to Deep Lake format is a great way to get started with datasets of increasing complexity.
- [Creating Time-Series Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-time-series-datasets.md): Deep Lake is a powerful tool for easily storing and sharing time-series datasets with your team.
- [Creating Datasets with Sequences](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-datasets-with-sequences.md): Deep Lake sequences are a powerful tool for storing temporal annotations such as bounding boxes in each frame of a video.
- [Creating Video Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models.md): Workflows for training models using Deep Lake datasets
- [Splitting Datasets for Training](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/splitting-datasets-training.md): How to Split Datasets for Training in Deep Lake
- [Training an Image Classification Model in PyTorch](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-classification-pytorch.md): Training an image classification model is a great way to get started with model training using Deep Lake datasets.
- [Training Models Using MMDetection](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-mmdet.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](https://docs-v3.activeloop.ai/examples/dl/tutorials/training-models/training-od-and-seg-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](https://docs-v3.activeloop.ai/examples/dl/tutorials/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](https://docs-v3.activeloop.ai/examples/dl/tutorials/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Deep Learning Playbooks](https://docs-v3.activeloop.ai/examples/dl/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](https://docs-v3.activeloop.ai/examples/dl/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](https://docs-v3.activeloop.ai/examples/dl/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](https://docs-v3.activeloop.ai/examples/dl/playbooks/training-reproducibility-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](https://docs-v3.activeloop.ai/examples/dl/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Deep Lake Dataloaders](https://docs-v3.activeloop.ai/examples/dl/dataloaders.md): Overview of Deep Lake's dataloader built and optimized in C++
- [API Summary](https://docs-v3.activeloop.ai/examples/dl/api.md): Summary of the most important low-level Deep Lake commands.
- [RAG](https://docs-v3.activeloop.ai/examples/rag.md): Using Deep Lake for Vector Store in RAG applications.
- [RAG Quickstart](https://docs-v3.activeloop.ai/examples/rag/quickstart.md): A jump-start guide to using Deep Lake for Vector Search.
- [RAG Tutorials](https://docs-v3.activeloop.ai/examples/rag/tutorials.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Store Basics](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-store-basics.md): Creating the Deep Lake Vector Store
- [Vector Search Options](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [LangChain API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Deep Lake Vector Store API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [Managed Database REST API](https://docs-v3.activeloop.ai/examples/rag/tutorials/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [Customizing Your Vector Store](https://docs-v3.activeloop.ai/examples/rag/tutorials/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Image Similarity Search](https://docs-v3.activeloop.ai/examples/rag/tutorials/image-similarity-search.md): Using Deep Lake for image similarity search
- [Improving Search Accuracy using Deep Memory](https://docs-v3.activeloop.ai/examples/rag/tutorials/deepmemory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [LangChain Integration](https://docs-v3.activeloop.ai/examples/rag/langchain-integration.md): Using Deep Lake as a Vector Store in LangChain
- [LlamaIndex Integration](https://docs-v3.activeloop.ai/examples/rag/llamaindex-integration.md): Using Deep Lake as a Vector Store in LlamaIndex
- [Managed Tensor Database](https://docs-v3.activeloop.ai/examples/rag/managed-database.md): Deep Lake Managed Database
- [REST API](https://docs-v3.activeloop.ai/examples/rag/managed-database/rest-api.md): How to Use the Deep Lake REST API
- [Migrating Datasets to the Tensor Database](https://docs-v3.activeloop.ai/examples/rag/managed-database/migrating-datasets-to-the-tensor-database.md): Migrating datasets to the Tensor Database
- [Deep Memory](https://docs-v3.activeloop.ai/examples/rag/deep-memory.md): Overview of Deep Lake tools for increasing retrieval accuracy
- [How it Works](https://docs-v3.activeloop.ai/examples/rag/deep-memory/how-it-works.md): Understanding Deep Memory
- [Tensor Query Language (TQL)](https://docs-v3.activeloop.ai/examples/tql.md): Deep Lake offers a performant SQL-style query engine for data analysis.
- [TQL Syntax](https://docs-v3.activeloop.ai/examples/tql/syntax.md): How to properly format TQL queries
- [Index for ANN Search](https://docs-v3.activeloop.ai/examples/tql/ann-index.md): Overview of Deep Lake's Index implementation for ANN search.
- [Caching and Optimization](https://docs-v3.activeloop.ai/examples/tql/ann-index/caching-and-optimization.md): Understanding Caching to Increase Query Performance in Deep Lake
- [Sampling Datasets](https://docs-v3.activeloop.ai/examples/tql/sampling.md): Implementation of samplers in TQL