From e46fc20bab4ab3885a28c46637b1db67f98c3d4d Mon Sep 17 00:00:00 2001 From: Jael Gu Date: Wed, 18 Sep 2024 13:38:30 +0800 Subject: [PATCH] Add more resources Signed-off-by: Jael Gu --- README.md | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/README.md b/README.md index 9e1fdf0..f5d4ba5 100644 --- a/README.md +++ b/README.md @@ -98,3 +98,16 @@ and generates a video embedding in numpy.ndarray. - labels: predicted class names. - scores: possibility scores ranking from high to low corresponding to predicted labels. - features: a video embedding in shape of (num_features,) representing features extracted by model. + + +# More Resources + +- [Understanding Class Activation Mapping (CAM) in Deep Learning - Zilliz blog](https://zilliz.com/learn/class-activation-mapping-CAM): Class Activation Mapping (CAM) is used to visualize and understand the decision-making of convolutional neural networks (CNNs) for computer vision tasks. +- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models. +- [What is a Convolutional Neural Network? An Engineer's Guide](https://zilliz.com/glossary/convolutional-neural-network): Convolutional Neural Network is a type of deep neural network that processes images, speeches, and videos. Let's find out more about CNN. +- [Understanding ImageNet: A Key Resource for Computer Vision and AI Research](https://zilliz.com/glossary/imagenet): The large-scale image database with over 14 million annotated images. Learn how this dataset supports advancements in computer vision. +- [Everything You Need to Know About Zero Shot Learning - Zilliz blog](https://zilliz.com/learn/what-is-zero-shot-learning): A comprehensive guide to Zero-Shot Learning, covering its methodologies, its relations with similarity search, and popular Zero-Shot Classification Models. +- [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. +- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database +- [Zilliz partnership with PyTorch - View image search solution tutorial](https://zilliz.com/partners/pytorch): Zilliz partnership with PyTorch \ No newline at end of file