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Photocredits: https://github.com/explodinggradients/ragas Ragas addresses a crucial challenge in the world of LLM applications: effectively assessing the performance of Retrieval Augmented Generation (RAG) pipelines. These pipelines leverage external data to enhance the context of LLMs, leading to potentially more accurate and informative responses. However, evaluating the effectiveness of these pipelines can be difficult. It is a
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Photocredits: https://github.com/uber/causalml Causal Machine Learning (Causal ML) is an advanced area of machine learning that focuses on understanding and modeling cause-and-effect relationships, rather than just correlations. Traditional ML models excel at finding patterns in data, but they often fall short in distinguishing between correlation and causation. Causal ML aims to fill this gap. For example,
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Photo credits: https://deci.ai/quantization-and-quantization-aware-training/ In the age of AI, bigger isn’t always better. While complex models with billions of parameters can achieve impressive results, their size poses challenges for deployment, especially on resource-constrained devices like smartphones and edge platforms. This is where quantization-aware training (QAT) comes in, offering a powerful solution for model compression without compromising
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Photocredits: https://kai-waehner.medium.com/kafka-native-machine-learning-and-model-deployment-c7df7e2a1d19 Machine learning (ML) has become an integral part of modern software systems, enabling businesses to extract valuable insights from data and make informed decisions. Apache Kafka, a distributed streaming platform, plays a crucial role in real-time ML applications by providing a high-throughput, low-latency platform for data ingestion and processing. While Kafka offers a
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Photocredits: https://www.ontotext.com/blog/graph-databases-interconected-data-relational-databases/ Imagine the world as a vast network where everything is connected. Understanding these connections and their complexities is crucial in today’s data-driven world. This is where heterogeneous graph learning comes in. What is Heterogeneous Graph Learning? Think of a social network like a spiderweb. People are the “nodes,” and their connections are the
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Photo credits: https://graphormer.readthedocs.io/en/latest/ A Graphormer is a deep learning architecture specifically designed for processing and analyzing graphs. It builds upon the success of the Transformer architecture, which has achieved remarkable results in natural language processing tasks. However, the Transformer architecture is primarily designed for sequential data (like text) and cannot directly handle graphs. Graphormer addresses
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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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