From 77f55eecf763906494b2cf0ea01e292743abc6b6 Mon Sep 17 00:00:00 2001 From: Hrittik Roy <67012359+hrittikhere@users.noreply.github.com> Date: Tue, 12 Dec 2023 23:53:40 +0530 Subject: [PATCH] [MAINTENANCE] Various typographical error fixes at `/examples` (#326) * [MAINTENANCE] Various typographical error fixes at `/examples` * [MAINTENANCE] Various typographical error fixes at `/examples` 2 --- docs/examples/index.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/examples/index.md b/docs/examples/index.md index 4e445a82..0d7c173e 100644 --- a/docs/examples/index.md +++ b/docs/examples/index.md @@ -1,11 +1,11 @@ 💡 Hands-on examples are the best way to learn about a new framework. -Here, you'll find several examples of how to build models with Ludwig for a variety of tasks with LLMs, neural networks, and tree-based models. We provide sample datasets, commands, scripts, and colab notebooks. Please [reach out](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) if you have any questions! +Here, you'll find several examples of how to build models with Ludwig for a variety of tasks with LLMs, neural networks, and tree-based models. We provide sample datasets, commands, scripts, and Colab notebooks. Please [reach out](https://join.slack.com/t/ludwig-ai/shared_invite/zt-mrxo87w6-DlX5~73T2B4v_g6jj0pJcQ) if you have any questions! -💬 The **LLMs** section gives a broad overview of the full breadth of Ludwig's LLM capabilities like [zero-shot batch inference]() and [fine-tuning Llama-2-7b](). +💬 The **LLMs** section gives a broad overview of the full breadth of Ludwig's LLM capabilities like [zero-shot batch inference](/examples/llms/llm_zero_shot_text_generation/) and [fine-tuning Llama-2-7b](/examples/llms/llm_finetuning/). -🎯 The **Supervised ML** section has in-depth tutorials for how to use Ludwig's command line interface and Python API for machine learning in a supervised setting. Check out [Image Classification on MNIST](http://127.0.0.1:8000/examples/mnist/). +🎯 The **Supervised ML** section has in-depth tutorials for how to use Ludwig's command line interface and Python API for machine learning in a supervised setting. Check out [Image Classification on MNIST](/examples/mnist/). -🏛️ The **Use Cases** section illustrates how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forcasting, multi-label classification to name just a few. Read about Ludwig models for [Sentiment Analysis](). +🏛️ The **Use Cases** section illustrates how Ludwig can be applied to a variety of machine learning tasks, such as, natural language understanding, timeseries forecasting, multi-label classification to name just a few. Read about Ludwig models for [Sentiment Analysis](/examples/sentiment_analysis/). Have fun exploring!