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LLMs and Transformers from Scratch: the Decoder | by Luís Roque
The article delves into the transformer’s decoder architecture, emphasizing the loop-like, iterative nature that contrasts with the linear processing of the encoder. It discusses the masked multi-head attention and encoder-decoder attention mechanisms, demonstrating their implementation in Python and NumPy through a translation example. The decoder’s role in Large Language Models (LLMs) is highlighted.
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Python to Rust: Discover Why Enums Are a Must-Use Feature!
The text explains the transition of a data scientist from Python to Rust, highlighting the significance of Enums in both languages. The author explores how Rust’s Enums offer more advanced features compared to Python and provides detailed comparisons of Enums, Option, and Result types in both languages. The author expresses excitement about the evolution of…
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Declarative vs Imperative Plotting with Python
The text provides an overview of imperative and declarative plotting in Python for beginners. It discusses the use of libraries such as Matplotlib, seaborn, Plotly Express, and hvplot for creating visualizations. The text details the characteristics, strengths, weaknesses, and examples of both imperative and declarative plotting styles. Different methods and techniques for creating various plots…
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Visualizing Everest Expeditions
Summary: The text discusses the process of gathering expedition data from The Himalayan Database and using it to create visualizations of Everest expeditions’ elevation profiles. It includes extracting and processing relevant data, reconstructing elevation profiles, and visualizing the waypoints. The process involves using Python for data processing, plotting, and Illustrator and Photoshop for final adjustments…
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Sklean Tutorial: Module 5
The text describes decision trees as simple. For further details, please refer to the full article on Towards Data Science.
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How to Build a Semantic Search Engine for Emojis
The article details the development of a semantic search engine for emojis, aiming to address the limitations of existing emoji search methods by incorporating both textual and visual information. The author outlines the challenges encountered and the strategies employed, ultimately creating a search engine that effectively navigates the overlap between two traditionally distinct modalities: images…
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Group Equivariant Self-Attention
The article discusses the integration of geometric priors into deep learning models, particularly focusing on the concept of group equivariance. It explains the benefits and the blueprint of geometric models, and introduces the application of group equivariant convolution and self-attention in the context of the transformer model. The article emphasizes the potential of group equivariant…
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120+ Best ChatGPT Prompts for Data Science
ChatGPT is a powerful analytical tool for data science, benefiting from AI capabilities and natural language processing. It excels in providing information, generating and explaining code, fostering idea generation, and supporting education and workflow automation. However, it has limitations in handling real-time data, interacting with databases, delving deep into advanced topics, potential bias, and personalized…
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Researchers from the University of Tubingen Propose SIGNeRF: A Novel AI Approach for Fast and Controllable NeRF Scene Editing and Scene-Integrated Object Generation
The research team at the University of Tübingen introduces SIGNeRF, a revolutionary approach for editing Neural Radiance Fields (NeRF) scenes. Utilizing generative 2D diffusion models, SIGNeRF enables rapid, precise, and consistent 3D scene modifications. Its remarkable performance is evidenced by its ability to integrate seamlessly, provide precise control, reduce complexity, and showcase versatility. This research…
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Meet aMUSEd: An Open-Source and Lightweight Masked Image Model (MIM) for Text-to-Image Generation based on MUSE
Text-to-image generation technology merges language and visuals in AI, facing challenges in efficiency and computational resources. Traditional models like latent diffusion are computationally intense. However, aMUSEd, a new innovative model, addresses these challenges with a lightweight design, reduced parameters, and unique architectural choices. It achieves high performance, offering practical viability and potential for diverse applications.