Adriano Soares Koshiyama

Here are the top insights from the Research Paper Reading Session on Saul LM 7b paper by Telmo Pessoa Pires. 📚✨ 1) Data Curation: We discussed the significance of meticulous data curation. To optimize model training with limited computational resources, it's essential to judiciously select and limit tokens. 📊 2) Data Cleaning Techniques: Understanding and efficiently addressing data cleaning is pivotal. We must have a comprehensive grasp of our data and identify necessary cleaning measures to enhance model performance. 🧹🔍 3) Avoiding Catastrophic Forgetting: Fine-tuning large language models often leads to catastrophic forgetting, where the model loses its proficiency in the original task. Telmo highlighted methods to mitigate this, preserving the model's initial learning while incorporating new data. 🧠🛡️ 4) Model Deployment Strategies: Efficient deployment of the fine-tuned model is key to translating research into practical applications. 🚀 Join our upcoming paper reading sessions for deep dives into cutting-edge research, insightful discussions, and expanding your knowledge horizons. Engage with experts, explore groundbreaking methodologies, and be at the forefront of innovation. 🌐🔬 Stay tuned for more updates on our next session! 📅📣 #researchpaperreadingsession #finetuning #mistral #llms #innovation #research

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How can we leverage LLMs to enhance or augment research workflows without diminishing the cognitive engagement of researchers? Read the full article by Lucas Soares now. #LLM #AI #DataScience

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📣 New MEAP release! 📣 Time Series Forecasting Using Foundation Models, by Marco Peixeiro. 📚 Stop wasting time coding your own time series forecasting models. With our latest MEAP release, "Time Series Forecasting Using Foundation Models", you will master efficient forecasting using powerful, pre-trained models built on billions of data points. Get ready to elevate your forecasting skills! 🚀 Take action now! Save 50% with code mlpeixeiro250li THE LINK TO THE BOOK IS IN THE COMMENTS SECTION. #TimeSeriesForecasting #DL #ML #ManningBooks

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In a recent article, Marco Peixeiro explored the architecture of Time-LLM and how it can effectively allow an LLM to predict time series data.

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New blog post out by the Bespoke AI team! Hallucination has become a blanket term. This blog post helps clarify and reframe LLM hallucinations in the context of more traditional NLP tasks. We’ve both been thinking this problem for a bit. We’ve been using Provenance-LLM as our primary hallucination detection validator, however we suspect that Minicheck performs better. @bespokelabsai's grounded factuality detector, Bespoke-Minicheck-7B, tops the LLM AggreFact leaderboard. This lightweight model outperforms larger foundation models, including GPT-4 and Mistral-Large 2 on grounded factuality detection. https://lnkd.in/g4tPCAKU

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Curious to learn about a new and promising time-series forecasting model? Marco Peixeiro's latest explainer focuses on SOFTS, a model that "employs a centralized strategy for learning interactions across different series, resulting in state-of-the-art performances in multivariate forecasting tasks."

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Curious to learn about a new and promising time-series forecasting model? Marco Peixeiro's latest explainer focuses on SOFTS, a model that "employs a centralized strategy for learning interactions across different series, resulting in state-of-the-art performances in multivariate forecasting tasks."

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🤖 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗕𝗲𝘆𝗼𝗻𝗱 𝘁𝗵𝗲 𝗕𝗼𝘁: 𝗪𝗵𝗮𝘁 𝗔𝗜 𝗦𝘁𝗶𝗹𝗹 𝗖𝗮𝗻'𝘁 𝗦𝗼𝗹𝘃𝗲 AI continues to dazzle with its capabilities, but there’s still plenty it can’t compute. Clare Walsh, Director of Education at IoA - Institute of Analytics, explores the gaps in AI’s reasoning and the crucial role of human expertise in bridging those limitations. Learn more about the future of AI-human collaboration and what we need to prioritise for the next wave of advancements. Read the full story, here 👉https://bit.ly/3ZDvh0G #AI #HumanIntelligence #Education #FutureOfWork

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📚 Understanding Statistical Learning Theory in Machine Learning 🧠 In his latest article, Rowen, our Machine Learning Algorithms Advocate, delves into the framework developed by Vladimir Vapnik and colleagues, which serves as the backbone of many machine learning algorithms today. From understanding the Vapnik-Chervonenkis (VC) dimension to mastering Support Vector Machines (SVMs), Rowen breaks down complex concepts into digestible insights. He also tackles crucial topics like overfitting, underfitting, and the bias-variance trade-off, making this article a must-read for anyone looking to strengthen their understanding of machine learning fundamentals. Check out the full article in the comment section ⬇️ #StatisticalLearningTheory #MachineLearning #SupportVectorMachines #AIAlgorithms #DataScience #AIPilipinasCebu #AIAdvocateProgram

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🔥 Read our Paper 📚 Model-Centric Integration of Uncertain Expert Knowledge into Importance Sampling-Based Parameter Estimation 🔗 https://lnkd.in/d4TrnyVp 👨‍🔬 by Éva Kenyeres and János Abonyi 🏫 University of Pannonia #expertknowledge #MonteCarlosimulation

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We are delighted to announce that Olivier Scaillet, GFRI's Director and Professor of Finance and Statistics, will be giving the keynote speech at the São Paulo School of Advanced Science in High-Dimensional Modelling. The program offers minicourses which aim to enhance specific aspects of data professionals' training, such as machine learning applications in finance, economics, and business, as well as training in cryptofinance. For more information: https://lnkd.in/eNTyw9yi University of Geneva Geneva School of Economics and Management - UNIGE SFI Swiss Finance Institute

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Pythonic's team of AI experts not only make the complex look simple, but excel in their knowledge, understanding, and application of AI across the ecosystem, and it's broader impact. Our latest blog from Senior NLP Scientist Fatemeh Rahimi highlights the recent NeurIPS conference: Shaping the Next Wave of AI and ML. https://hubs.ly/Q02vWkm40 #pythonicexpertise #pythonicai #neurIPS2023 #AIinfluencer #pythonicleader #aifortitle #ai #pythonicexperts #nlpscientist

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Thomas Wood of Fast Data Science will be with Bettina Moltrecht, PhD at MethodsCon: Futures in Manchester, run by the National Centre for Research Methods on 11 and 12 September 2024 to present Harmony, the NLP and AI tool we have been developing for researchers in social science, funded by Wellcome Trust and ESRC: Economic and Social Research Council 🌐 https://lnkd.in/drXNbRK #predictiveanalytics #dataanalytics #analytics #nlp #machinelearning #fastdatascience #neuralnetworks #artificialintelligence #bigdata #ai #datascience #ml #naturallanguageprocessing

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Check out the latest blog from one of our Data Surgeons, Nathan Rayens, PhD, as he discusses 5 Key Elements for Responsible AI. At Data Surge, we believe this topic is so important in our fast-paced world. Interested in learning more, email us at [email protected]. #AI #ML #ResponsibleAI #DataModernization

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[NEW COUPON] Econometrics and Statistics for Business in R & Python Course Learn Causal Inference & Statistical Modeling to solve finance and marketing business problems in Python and R Created by Diogo Alves de Resende | 10 hours on-demand video course | 2 downloadable resources Econometrics for Business Course Overview Econometrics and Statistics for Business in R & Python Course Coupon Code. Econometrics has horrible fame. The complex theorems, combined with boring classes where it feels like you are learning Greek, give every student nightmares. This course stays away from that. It will focus on (1) giving you the intuition and tools to apply the techniques learned, (2) making sure everything that you learn is actionable in your career, and (3) offer you a tool kit of peer-reviewed econometric causal inference techniques that will make you stand out and give you the ability to answer the tough questions. Get Udemy Coupon Code: https://lnkd.in/d3Nf2ASb #Econometrics #Business #Python 

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In this article, Marco Peixeiro explores the inner workings of Moirai and apply the model in a forecasting project (using Python) and compare its performance to data-specific deep learning models:

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