- [Understanding Neural Network Regularization and Key Regularization Techniques - Zilliz blog](https://zilliz.com/learn/understanding-regularization-in-nueral-networks): Regularization prevents a machine-learning model from overfitting during the training process. We'll discuss its concept and key regularization techniques.
@ -68,4 +70,3 @@ The `towhee/resnet-image-embedding` Operator implements the function of image em
- [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.
- [Training Text Embeddings with Jina AI - Zilliz blog](https://zilliz.com/blog/training-text-embeddings-with-jina-ai): In a recent talk by Bo Wang, he discussed the creation of Jina text embeddings for modern vector search and RAG systems. He also shared methodologies for training embedding models that effectively encode extensive information, along with guidance o
