Update README.md fixed links

tutorials/README.md

@@ -38,7 +38,7 @@ You can find a nice tutorial for using managed notebooks [here](https://codelabs
 ## **Artificial Intelligence and Machine Learning** <a name='ml'></a>
 Machine learning is a subfield of artificial intelligence that focuses on the development of algorithms and models that enable computers to learn from and make predictions or decisions based on data, without being explicitly programmed. Machine learning on GCP generally occurs within VertexAI. You can learn more about machine learning on GCP at this [Google Crash Course](https://developers.google.com/machine-learning/crash-course). For hands-on examples, try out [this module](https://github.com/NIGMS/COVIDMachineLearningSFSU) developed by San Francisco State University or [this one from the University of Arkasas](https://github.com/NIGMS/MachineLearningUA) developed for the NIGMS Sandbox Project.
 
-Now that the age of **Generative AI** (Gen AI) has arrived, Google has released a host of Gen AI offerings within the Vertex AI suite. Some examples of what generative AI models are capabile of are extracting wanted information from text, transforming speech into text, generating images from describtions and vice versa, and much more. Vertex AI's [Generative AI Studio](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/generative-ai-studio) console allows the user to rapidly create, test, and train generative AI models on the cloud in a safe and secure setting. See our overview in [this tutorial](/tutorials/notebooks/GenAI/GenAIStudioGCP.ipynb). The studio also has ready-to-use models all contained with in the [Model Garden](https://cloud.google.com/vertex-ai/docs/start/explore-models). These models range from foundation models, fine-tunable models, and task-specific solutions. To learn more about Gen AI on GCP take a look at our [GenAI tutorials](/tutorials/notebooks/GenAI) that go over [Gemini](/tutorials/notebooks/GenAI/Gemini_intro.ipynb), [RAG](/tutorials/notebooks/GenAI/GCP_Pubmed_chatbot.ipynb), [Langchain](/tutorials/notebooks/GenAI/langchain_on_vertex.ipynb), [training via Huggingface](/tutorials/notebooks/GenAI/GenAIStudioGCP.ipynb), and more! The Google github also provides many generative AI tutorials hosted on [GitHub](https://github.com/GoogleCloudPlatform/generative-ai/tree/main). Some example they provide are under [language here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/language).
+Now that the age of **Generative AI** (Gen AI) has arrived, Google has released a host of Gen AI offerings within the Vertex AI suite. Some examples of what generative AI models are capabile of are extracting wanted information from text, transforming speech into text, generating images from describtions and vice versa, and much more. Vertex AI's [Vertex AI Studio](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/generative-ai-studio) console allows the user to rapidly create, test, and train generative AI models on the cloud in a safe and secure setting. See our overview in [this tutorial](/tutorials/notebooks/GenAI/VertexAIStudioGCP.ipynb). The studio also has ready-to-use models all contained with in the [Model Garden](https://cloud.google.com/vertex-ai/docs/start/explore-models). These models range from foundation models, fine-tunable models, and task-specific solutions. To learn more about Gen AI on GCP take a look at our [GenAI tutorials](/tutorials/notebooks/GenAI) that go over [Gemini](/tutorials/notebooks/GenAI/Gemini_Intro.ipynb), [RAG](/tutorials/notebooks/GenAI/GCP_Pubmed_chatbot.ipynb), [Langchain](/tutorials/notebooks/GenAI/langchain_on_vertex.ipynb), [training via Huggingface](/tutorials/notebooks/GenAI/GCP_GenAI_Huggingface.ipynb), and more! The Google github also provides many generative AI tutorials hosted on [GitHub](https://github.com/GoogleCloudPlatform/generative-ai/tree/main). Some example they provide are under [language here](https://github.com/GoogleCloudPlatform/generative-ai/tree/main/language).
 
 ## **Medical Image Segmentation** <a name="mi"></a>
 Medical image analysis is the application of computational algorithms and techniques to extract meaningful information from medical images for diagnosis, treatment planning, and research purposes. Medical image analysis requires large image files and often elastic storage and accelerated computing.