Machine Learning

Welcome to the Machine Learning Hub, your one-stop destination for all things related to machine learning!

Get ready to embark on an exciting journey into the realm of AI and discover how machines can learn and make intelligent decisions. Our blog articles are crafted with simplicity and clarity in mind, making complex machine learning concepts easy to understand for everyone. Whether you’re a beginner or an experienced practitioner, we’ve got you covered with informative and insightful content. Explore the fascinating world of algorithms, models, and data as we delve into supervised and unsupervised learning, reinforcement learning, and more. Discover practical applications in various domains like healthcare, finance, and autonomous vehicles.  From introductory guides to advanced techniques, we’re here to help you demystify machine learning and unlock its potential. Join us on this journey as we unravel the secrets of machine learning and empower you to build intelligent systems that can analyze data, make predictions, and drive innovation.

Let’s shape the future together with the power of machine learning!

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Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models | by Jérôme DIAZ | Dec, 2024

In this article we will explore why 128K tokens (and more) models can’t fully replace using RAG. We’ll start with a brief reminder of the problems that can be solved with RAG, before looking at the improvements in LLMs and their impact on the need to use RAG. Illustration by the author. RAG isn’t really …

Why Retrieval-Augmented Generation Is Still Relevant in the Era of Long-Context Language Models | by Jérôme DIAZ | Dec, 2024 Read More »

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Missing Data in Time-Series: Machine Learning Techniques | by Sara Nóbrega | Dec, 2024

Part 1: Leverage linear regression and decision trees to impute time-series gaps. Source: DALL-E. Missing data in time-series analysis — sounds familiar? Does missing data in your datasets due to malfunctioning sensors, transmission, or any kind of maintenance sound all too familiar to you? Well, missing values derail your forecast and skew your analysis. So, …

Missing Data in Time-Series: Machine Learning Techniques | by Sara Nóbrega | Dec, 2024 Read More »

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Genie 2: A large-scale foundation world model

Acknowledgements Genie 2 was led by Jack Parker-Holder with technical leadership by Stephen Spencer, with key contributions from Philip Ball, Jake Bruce, Vibhavari Dasagi, Kristian Holsheimer, Christos Kaplanis, Alexandre Moufarek, Guy Scully, Jeremy Shar, Jimmy Shi and Jessica Yung, and contributions from Michael Dennis, Sultan Kenjeyev and Shangbang Long. Yusuf Aytar, Jeff Clune, Sander Dieleman, …

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Streamline Your Workflow when Starting a New Research Paper | by Rodrigo M Carrillo Larco, MD, PhD | Dec, 2024

Python code to create folders and Word documents for research papers in biomedical sciences — all in one go with only two inputs Photo by Maksym Kaharlytskyi on Unsplash I am a researcher with over seven years of experience working in public health and epidemiological research. Every time I am about to start a new …

Streamline Your Workflow when Starting a New Research Paper | by Rodrigo M Carrillo Larco, MD, PhD | Dec, 2024 Read More »

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GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy

Technologies Published 4 December 2024 Authors Ilan Price and Matthew Willson New AI model advances the prediction of weather uncertainties and risks, delivering faster, more accurate forecasts up to 15 days ahead Weather impacts all of us — shaping our decisions, our safety, and our way of life. As climate change drives more extreme weather …

GenCast predicts weather and the risks of extreme conditions with state-of-the-art accuracy Read More »

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Combining Large and Small LLMs to Boost Inference Time and Quality | by Richa Gadgil | Dec, 2024

Implementing Speculative and Contrastive Decoding Large Language models are comprised of billions of parameters (weights). For each word it generates, the model has to perform computationally expensive calculations across all of these parameters. Large Language models accept a sentence, or sequence of tokens, and generate a probability distribution of the next most likely token. Thus, …

Combining Large and Small LLMs to Boost Inference Time and Quality | by Richa Gadgil | Dec, 2024 Read More »

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Google DeepMind at NeurIPS 2024

Research Published 5 December 2024 Advancing adaptive AI agents, empowering 3D scene creation, and innovating LLM training for a smarter, safer future Next week, AI researchers worldwide will gather for the 38th Annual Conference on Neural Information Processing Systems (NeurIPS), taking place December 10-15 in Vancouver, Two papers led by Google DeepMind researchers will be …

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Introducing Univariate Exemplar Recommenders: how to profile Customer Behavior in a single vector | by Michelangiolo Mazzeschi | Dec, 2024

5. Univariate sequential encoding It is time to build the sequential mechanism to keep track of user choices over time. The mechanism I idealized works on two separate vectors (that after the process end up being one, hence univariate), a historical vector and a caching vector. The historical vector is the one that is used …

Introducing Univariate Exemplar Recommenders: how to profile Customer Behavior in a single vector | by Michelangiolo Mazzeschi | Dec, 2024 Read More »

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Watermarking AI-generated text and video with SynthID

Technologies Published 14 May 2024 Announcing our novel watermarking method for AI-generated text and video, and how we’re bringing SynthID to key Google products Generative AI tools — and the large language model technologies behind them — have captured the public imagination. From helping with work tasks to enhancing creativity, these tools are quickly becoming …

Watermarking AI-generated text and video with SynthID Read More »

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