diff --git a/README.md b/README.md
index 1b381a7..0b9554d 100644
--- a/README.md
+++ b/README.md
@@ -104,3 +104,16 @@ Takes in a numpy rgb image in channels first. It transforms input into animated
 ​	The new image.
 
 
+
+
+    # More Resources
+
+    - [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
+- [What is Llama 2?](https://zilliz.com/glossary/llama2): Learn all about Llama 2, get  how to create vector embeddings, and more.
+- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
+- [Zilliz-Hugging Face partnership - Explore transformer data model repo](https://zilliz.com/partners/hugging-face): Use Hugging Face’s community-driven repository of data models to convert unstructured data into embeddings to store in Zilliz Cloud, and access a code tutorial.
+- [What is a Generative Adversarial Network? An Easy Guide](https://zilliz.com/glossary/generative-adversarial-networks): Just like we classify animal fossils into domains, kingdoms, and phyla, we classify AI networks, too. At the highest level, we classify AI networks as "discriminative" and "generative." A generative neural network is an AI that creates something new. This differs from a discriminative network, which classifies something that already exists into particular buckets. Kind of like we're doing right now, by bucketing generative adversarial networks (GANs) into appropriate classifications.
+So, if you were in a situation where you wanted to use textual tags to create a new visual image, like with Midjourney, you'd use a generative network. However, if you had a giant pile of data that you needed to classify and tag, you'd use a discriminative model.
+- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar/success): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
+- [Zilliz partnership with PyTorch - View image search solution tutorial](https://zilliz.com/partners/pytorch): Zilliz partnership with PyTorch
+    
\ No newline at end of file