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Fixed broken links in README #2769

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Fixed broken links in README #2769

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6272f84
Fixed broken links in README
DeVcB13d Feb 14, 2024
68e0d73
Merge branch 'main' into fix_readme_links
DeVcB13d Feb 18, 2024
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12 changes: 6 additions & 6 deletions README.md
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Expand Up @@ -97,24 +97,24 @@ pip3 install deeplake
Using Deep Lake as a Vector Store for building LLM applications:
### - [Vector Store Quickstart](https://docs.activeloop.ai/quickstart)
### - [Vector Store Getting Started Guide](https://docs.activeloop.ai/getting-started/vector-store)
### - [Using Deep Lake with LangChain](https://docs.activeloop.ai/tutorials/vector-store/deep-lake-vector-store-in-langchain)
### - [Image Similarity Search with Deep Lake](https://docs.activeloop.ai/tutorials/vector-store/image-similarity-search)
### - [Using Deep Lake with LangChain](https://docs.activeloop.ai/example-code/tutorials/vector-store/deep-lake-vector-store-in-langchain)
### - [Image Similarity Search with Deep Lake](https://docs.activeloop.ai/example-code/tutorials/vector-store/image-similarity-search)


### Deep Learning Applications
Using Deep Lake for managing data while training Deep Learning models:
### - [Deep Learning Quickstart](https://docs.activeloop.ai/quickstart-dl)
### - [Deep Learning Getting Started Guide](https://docs.activeloop.ai/getting-started/deep-learning)
### - [Tutorials for Training Models](https://docs.activeloop.ai/tutorials/deep-learning/training-models)
### - [Tutorials for Creating Deep Learning Datasets](https://docs.activeloop.ai/tutorials/deep-learning/creating-datasets)
### - [Deep Learning Playbooks](https://docs.activeloop.ai/playbooks/evaluating-model-performance)
### - [Tutorials for Training Models](https://docs.activeloop.ai/example-code/tutorials/deep-learning/training-models)
### - [Tutorials for Creating Deep Learning Datasets](https://docs.activeloop.ai/example-code/tutorials/deep-learning/creating-datasets)
### - [Deep Learning Playbooks](https://docs.activeloop.ai/example-code/playbooks)

## 鈿欙笍 Integrations

Deep Lake offers integrations with other tools in order to streamline your deep learning workflows. Current integrations include:

* **Model Training**
* Stream data while training thousands of pre-built models using [MMDetection](https://github.com/open-mmlab/mmdetection), a popular open-source object detection toolbox based on PyTorch. Learn more in [this tutorial](https://docs.activeloop.ai/tutorials/training-models/training-models-using-mmdetection).
* Stream data while training thousands of pre-built models using [MMDetection](https://github.com/open-mmlab/mmdetection), a popular open-source object detection toolbox based on PyTorch. Learn more in [this tutorial](https://docs.activeloop.ai/example-code/tutorials/deep-learning/training-models/training-models-using-mmdetection).

* **Experiment Tracking**
* Track experiments and achieve full model reproducibility using Deep Lake and [Weights & Biases](https://wandb.ai/). Our integration automatically pushes dataset-related information (uri, commit hash, view id) to your W&B runs. Further details are available [in our model-reproducibility playbook](https://docs.activeloop.ai/playbooks/training-reproducibility-with-wandb).
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