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Photocredits :https://arxiv.org/pdf/2208.07638.pdf Knowledge graphs (KGs) have emerged as a powerful tool for representing and reasoning over semantic knowledge. They consist of entities, relationships, and attributes, which can be used to model a wide variety of domains, such as science, medicine, and finance. However, the inherent shallowness and static nature of KG embeddings limit their ability…
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Photocredits: https://positivethinking.tech/insights/llm-mini-series-parallel-multi-document-question-answering-with-llama-index-and-retrieval-augmented-generation/ RAG’s unique ability to combine information retrieval with sequence generation has opened up new frontiers for developing systems that can provide comprehensive and contextually relevant answers. A Practical Implementation of RAG This article delves into a practical implementation of RAG using the Hugging Face Transformers library, showcasing a step-by-step process of utilizing RAG’s…
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Photocredit:https://towhee.io/tasks/detail/pipeline/retrieval-augmented-generation Retrieval Augmented Generation (RAG) is a text generation technique that combines retrieval and generation. It works by first retrieving relevant documents from a large corpus of text, and then using those documents to generate a new text. RAG can be used to generate a variety of text formats, including summaries, translations, and creative text…
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Photo Credits:https://www.mdpi.com/2313-433X/9/3/69 Generative adversarial networks (GANs) are a type of machine learning model that can be used to generate synthetic data. GANs work by pitting two neural networks against each other: a generator and a discriminator. The generator tries to create synthetic data that is indistinguishable from real data, while the discriminator tries to distinguish…
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Photocredits: https://www.semanticscholar.org/paper/GNNExplainer%3A-Generating-Explanations-for-Graph-Ying-Bourgeois/00358a3f17821476d93461192b9229fe7d92bb3f Graph neural networks (GNNs) are a powerful type of machine learning model that can be used to learn from and make predictions on graph-structured data. GNNs are used in a wide variety of applications, including social network analysis, fraud detection, and drug discovery. However, GNNs can also be complex and difficult to understand.…
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Photocredits: https://www.researchgate.net/figure/Explaining-predictions-of-an-AI-system-using-SA-and-LRP-Image-courtesy-of-W-Samek-15_fig7_336131051 Deep learning models have become increasingly powerful and successful in recent years, but they can also be complex and difficult to understand. This can make it challenging to trust their decisions, especially in critical applications where it is important to know why a model made a particular prediction. The increasing complexity of deep…
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Photocredits: https://realkm.com/2023/03/06/introduction-to-knowledge-graphs-part-2-history-of-knowledge-graphs/ Data representation and organization has witnessed significant evolution, especially in the last few decades. Central to this evolution is the concept of the Knowledge Graph (KG). But where did it all start? And how did we progress from simple linked data structures known as semantic webs to today’s sophisticated AI-integrated knowledge graphs? The…
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Photocredits: https://www.tigergraph.com/blog/understanding-graph-embeddings/ Knowledge graphs (KGs) provide a structured and semantically rich way to represent knowledge by capturing entities and their relationships. While KGs are valuable in capturing relational information, many machine learning models require data in vector form. That’s where embeddings come into play. What are Embeddings? Embeddings are dense vector representations of data, which…
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Photo credits: https://paperswithcode.com/method/multi-head-attention Multi-head attention is a mechanism that allows a model to focus on different parts of its input sequence, from different perspectives. It is a key component of the Transformer architecture, which is a state-of-the-art model for natural language processing (NLP) tasks such as machine translation, text summarization, and question answering. How multi-head…
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Photo Credits: https://www.kaggle.com/code/residentmario/transformer-architecture-self-attention Self-attention is a mechanism that allows a model to focus on different parts of its input sequence. It is a key component of the Transformer architecture, which is a state-of-the-art model for natural language processing (NLP) tasks such as machine translation, text summarization, and question answering. How self-attention works Self-attention works by…
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