# EXAMPLE CODE

- [Getting Started](/v3.8.2/example-code/getting-started.md): Comprehensive guides for getting started with Deep Lake
- [Vector Store](/v3.8.2/example-code/getting-started/vector-store.md): The comprehensive guide for Deep Lake in Vector Storage and Search applications.
- [Step 1: Hello World](/v3.8.2/example-code/getting-started/vector-store/step-1-hello-world.md): Installing Deep Lake
- [Step 2: Creating Deep Lake Vector Stores](/v3.8.2/example-code/getting-started/vector-store/step-2-creating-deep-lake-vector-stores.md): Creating the Deep Lake Vector Store
- [Step 3: Performing Search in Vector Stores](/v3.8.2/example-code/getting-started/vector-store/step-3-performing-search-in-the-vector-store.md): Running search in the Deep Lake Vector Store
- [Step 4: Customizing Vector Stores](/v3.8.2/example-code/getting-started/vector-store/step-4-customizing-vector-stores.md): Customizing the Deep Lake Vector Store
- [Deep Learning](/v3.8.2/example-code/getting-started/deep-learning.md): The comprehensive guide for Deep Lake in Deep Learning applications.
- [Step 1: Hello World](/v3.8.2/example-code/getting-started/deep-learning/hello-world.md): Installing Deep Lake and accessing your first Deep Lake Dataset.
- [Step 2: Creating Deep Lake Datasets](/v3.8.2/example-code/getting-started/deep-learning/creating-datasets-manually.md): Creating and storing Deep Lake Datasets.
- [Step 3: Understanding Compression](/v3.8.2/example-code/getting-started/deep-learning/understanding-compression.md): Using compression to achieve optimal performance in Deep Lake.
- [Step 4: Accessing and Updating Data](/v3.8.2/example-code/getting-started/deep-learning/accessing-datasets.md): Learn how Deep Lake Datasets can be accessed or loaded from a variety of storage locations.
- [Step 5: Visualizing Datasets](/v3.8.2/example-code/getting-started/deep-learning/visualizing-datasets.md): Visualizing and inspecting your datasets.
- [Step 6: Using Activeloop Storage](/v3.8.2/example-code/getting-started/deep-learning/using-activeloop-storage.md): Storing and loading datasets from Deep Lake Storage.
- [Step 7: Connecting Deep Lake Datasets to ML Frameworks](/v3.8.2/example-code/getting-started/deep-learning/connecting-to-ml-frameworks.md): Connecting Deep Lake Datasets to machine learning frameworks such as PyTorch and TensorFlow.
- [Step 8: Parallel Computing](/v3.8.2/example-code/getting-started/deep-learning/parallel-computing.md): Running computations and processing data in parallel.
- [Step 9: Dataset Version Control](/v3.8.2/example-code/getting-started/deep-learning/dataset-version-control.md): Managing changes to your datasets using Version Control.
- [Step 10: Dataset Filtering](/v3.8.2/example-code/getting-started/deep-learning/dataset-filtering.md): Filtering datasets using user-defined-functions or SQL-style queries.
- [Tutorials (w Colab)](/v3.8.2/example-code/tutorials.md): Common workflows with Deep Lake (includes Colab notebooks)
- [Vector Store Tutorials](/v3.8.2/example-code/tutorials/vector-store.md): Tutorials for using Deep Lake in Vector Store applications
- [Vector Search Options](/v3.8.2/example-code/tutorials/vector-store/vector-search-options.md): Overview of Vector Search Options in Deep Lake
- [Deep Lake Vector Store API](/v3.8.2/example-code/tutorials/vector-store/vector-search-options/deep-lake-vector-store-api.md): Running Vector Search in the Deep Lake Vector Store module.
- [REST API](/v3.8.2/example-code/tutorials/vector-store/vector-search-options/rest-api.md): Running Vector Search in the Deep Lake Tensor Database using the REST API
- [LangChain API](/v3.8.2/example-code/tutorials/vector-store/vector-search-options/langchain-api.md): Vector Search using Deep Lake in LangChain
- [Image Similarity Search](/v3.8.2/example-code/tutorials/vector-store/image-similarity-search.md): Using Deep Lake for image similarity search
- [Deep Lake Vector Store in LangChain](/v3.8.2/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain.md): Using Deep Lake as a Vector Store in LangChain
- [Improving Search Accuracy using Deep Memory](/v3.8.2/example-code/tutorials/vector-store/improving-search-accuracy-using-deep-memory.md): Using Deep Memory to improve the accuracy of your Vector Search
- [Deep Learning Tutorials](/v3.8.2/example-code/tutorials/deep-learning.md): Tutorials for using Deep Lake in deep-learning applications.
- [Creating Datasets](/v3.8.2/example-code/tutorials/deep-learning/creating-datasets.md): Workflows for creating Deep Lake datasets
- [Creating Complex Datasets](/v3.8.2/example-code/tutorials/deep-learning/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](/v3.8.2/example-code/tutorials/deep-learning/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](/v3.8.2/example-code/tutorials/deep-learning/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](/v3.8.2/example-code/tutorials/deep-learning/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](/v3.8.2/example-code/tutorials/deep-learning/creating-datasets/creating-video-datasets.md): Get started with video datasets using Deep Lake.
- [Training Models](/v3.8.2/example-code/tutorials/deep-learning/training-models.md): Workflows for training models using Deep Lake datasets
- [Training an Image Classification Model in PyTorch](/v3.8.2/example-code/tutorials/deep-learning/training-models/training-an-image-classification-model-in-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](/v3.8.2/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection.md): How to Train Deep Learning models using Deep Lake's integration with MMDetection
- [Training Models Using PyTorch Lightning](/v3.8.2/example-code/tutorials/deep-learning/training-models/training-models-using-pytorch-lightning.md): How to Train models using Deep Lake and PyTorch Lightning
- [Training on AWS SageMaker](/v3.8.2/example-code/tutorials/deep-learning/training-models/training-on-aws-sagemaker.md): How to Train models on AWS SageMaker using Deep Lake datasets
- [Training an Object Detection and Segmentation Model in PyTorch](/v3.8.2/example-code/tutorials/deep-learning/training-models/training-an-object-detection-and-segmentation-model-in-pytorch.md): Training an object detection and segmentation model is a great way to learn about complex data preprocessing for training models.
- [Updating Datasets](/v3.8.2/example-code/tutorials/deep-learning/updating-datasets.md): Updating Deep Lake datasets
- [Data Processing Using Parallel Computing](/v3.8.2/example-code/tutorials/deep-learning/data-processing-using-parallel-computing.md): Deeplake offers built-in methods for parallelizing dataset computations in order to achieve faster data processing.
- [Concurrent Writes](/v3.8.2/example-code/tutorials/concurrent-writes.md): Concurrent writes in Deep Lake
- [Concurrency Using Zookeeper Locks](/v3.8.2/example-code/tutorials/concurrent-writes/concurrency-using-zookeeper-locks.md): Using Zookeeper for locking Deep Lake datasets.
- [Playbooks](/v3.8.2/example-code/playbooks.md): How to perform complex workflows using Deep Lake.
- [Querying, Training and Editing Datasets with Data Lineage](/v3.8.2/example-code/playbooks/training-with-lineage.md): How to use queries and version control while training models.
- [Evaluating Model Performance](/v3.8.2/example-code/playbooks/evaluating-model-performance.md): How to compare ground-truth annotations with model predictions
- [Training Reproducibility Using Deep Lake and Weights & Biases](/v3.8.2/example-code/playbooks/training-reproducibility-with-wandb.md): How to achieve full reproducibility of model training using Deep Lake and W\&B
- [Working with Videos](/v3.8.2/example-code/playbooks/working-with-videos.md): How manage video datasets and train models using Deep Lake.
- [Low-Level API Summary](/v3.8.2/example-code/api-basics.md): Summary of the most important low-level Deep Lake commands.