Thibaud BARREAU

❗ 𝗧𝗛𝗜𝗦 𝗪𝗘𝗘𝗞 𝗧𝗛𝗨𝗥𝗦𝗗𝗔𝗬 💫𝗙𝗿𝗼𝗺 𝗠𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝘁𝗼 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗶𝗺𝗮𝗴𝗲 𝗮𝗻𝗱 𝘁𝗲𝘅𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 💫 📅𝗗𝗮𝘁𝗲: 17 October 2024 🕝𝗧𝗶𝗺𝗲: 15:00 CEST I 14:00 BST I 09:00 EST 🗺️𝗟𝗼𝗰𝗮𝘁𝗶𝗼𝗻: Online ⌛𝗗𝘂𝗿𝗮𝘁𝗶𝗼𝗻: 1 hour 🆓Registration: 𝗙𝗿𝗲𝗲 From Modelling to Deep Learning: Advanced image and text analysis | JMP Do you collect 𝗶𝗺𝗮𝗴𝗲 𝗮𝗻𝗱/𝗼𝗿 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘁𝗲𝘅𝘁 𝗱𝗮𝘁𝗮 as part of your research, development, or production activities? Are you finding 𝗺𝗮𝗻𝘂𝗮𝗹 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 of such 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮 to be difficult, tedious, or error-prone? You’ve spent 𝘃𝗮𝗹𝘂𝗮𝗯𝗹𝗲 𝘁𝗶𝗺𝗲 and 𝗲𝗳𝗳𝗼𝗿𝘁 collecting your data sets, and now want to put them to constructive use. You are therefore interested in reliably recognizing patterns from your 𝗶𝗺𝗮𝗴𝗲𝘀 or 𝘁𝗲𝘅𝘁 responsible for these defects – and doing so in a 𝘁𝗶𝗺𝗲𝗹𝘆 𝗺𝗮𝗻𝗻𝗲𝗿. In this webinar, we introduce some novel ways to 𝗮𝗻𝗮𝗹𝘆𝘇𝗲 𝗶𝗺𝗮𝗴𝗲 and 𝘁𝗲𝘅𝘁 𝗱𝗮𝘁𝗮 to aid in 𝗰𝗹𝗮𝘀𝘀𝗶𝗳𝘆𝗶𝗻𝗴, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗻𝗴, and 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 using 𝗱𝗲𝗲𝗽 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗺𝗲𝘁𝗵𝗼𝗱𝘀 that are simple to apply and master. The 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 and 𝗺𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝘁𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀 within 𝗝𝗠𝗣 𝗣𝗿𝗼 are powerful for analyzing tabular or spreadsheet data common in R&D and manufacturing. A new add-in for use with JMP Pro allows users to extend this functionality to 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 and 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 capable of image and text analysis. Using case studies, you can transform your data into meaningful insights, thus achieving better outcomes. 𝙲̲𝚊̲𝚜̲𝚎̲ ̲𝚜̲𝚝̲𝚞̲𝚍̲𝚒̲𝚎̲𝚜̲ ̲𝚒̲𝚗̲𝚌̲𝚕̲𝚞̲𝚍̲𝚎̲:̲ • 𝘗𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘯𝘨 𝘮𝘰𝘭𝘦𝘤𝘶𝘭𝘢𝘳 𝘱𝘳𝘰𝘱𝘦𝘳𝘵𝘪𝘦𝘴 𝘧𝘳𝘰𝘮 𝘚𝘔𝘐𝘓𝘌𝘚 𝘴𝘵𝘳𝘪𝘯𝘨𝘴. • 𝘗𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘯𝘨 𝘥𝘦𝘧𝘦𝘤𝘵𝘴 𝘧𝘳𝘰𝘮 𝘸𝘢𝘧𝘦𝘳 𝘮𝘢𝘱 𝘪𝘮𝘢𝘨𝘦𝘴. • 𝘗𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘯𝘨 𝘴𝘦𝘯𝘵𝘪𝘮𝘦𝘯𝘵 𝘧𝘳𝘰𝘮 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘳𝘦𝘷𝘪𝘦𝘸𝘴. • 𝘗𝘳𝘦𝘥𝘪𝘤𝘵𝘪𝘯𝘨 𝘳𝘦𝘭𝘦𝘷𝘢𝘯𝘤𝘦 𝘧𝘳𝘰𝘮 𝘴𝘤𝘪𝘦𝘯𝘵𝘪𝘧𝘪𝘤 𝘵𝘪𝘵𝘭𝘦𝘴 𝘢𝘯𝘥 𝘢𝘣𝘴𝘵𝘳𝘢𝘤𝘵𝘴. 👇This webinar will be of interest to anyone working in 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵, 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁, or 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 who is collecting and wishing to make better use of 𝘂𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗱𝗮𝘁𝗮. You will gain an understanding of what is possible with modern and easy to apply deep learning methods to aid richer understanding and better decisions from your unstructured data.  Spyros Megalou Cristina Cosenza Craige Nyatanga Russ Wolfinger David Barnett #dataanalysis #machinelearning #deeplearning #artificialintelligence #dataanalytics #datascience #analytics #statistics #research #JMP #JMPPro #JMPsoftware #JMPcommunity #manufacturing #engineering #researchanddevelopment #qualitycontrol #predictiveanalytics #imageanalysis #naturallanguageprocessing #cropscience #formulation #chemistry #agronomy

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Dear Network, As an Autodidactic and loves Reverse Engineering this don't make sense in my mind. Can anyone explain how Anthropic Claude is building a Deep Learning Dashboard with such ease and speed ? How does a predictive model creates such thing ? I would love your point of view to verify the truth of the output it gives me. Your valuable contribution would be appreciated to make my understanding thorough. P:S: Ignore the Music as much as you can :xD #Claude #DeepLearningVisualizer #Anthropic #GenerativeAI

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Forget about LLMs Ranking Leaderboards🥇, Welcome to the Era of Model Routing 🔀! This shift in the market is truly transformative. The idea of dynamically selecting the most suitable model for each specific task is a game-changer. It's like having a garage full of specialized vehicles, each ready to take on a different terrain. Whether it's the speed of a Ferrari 🏎 for complex technical analyses or the reliability of a Range Rover 🚗 for varied tasks, model routing ensures the optimal performance for every scenario. Amazon Q for Business is a perfect example, dynamically choosing the best model based on query complexity to maximize user experience. This approach heralds a future where model selection is flexible, task-specific, and efficient, marking a significant departure from static model use. Exciting times ahead in the world of generative AI! #AI #MachineLearning #ModelRouting #Innovation #TechRevolution #FutureOfAI #GenerativeAI #SmartTech 🚀🔍🧠

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Hugging Face x LangChain : A new partner package in LangChain - We are thrilled to announce the launch of langchain_huggingface, a partner package in LangChain jointly maintained by Hugging Face and LangChain. This new Python package is designed to bring the power of the latest development of Hugging Face into LangChain and keep it up to date. langchain-huggingface integrates seamlessly with LangChain, providing an efficient and effective way to utilize Hugging Face models within the LangChain ecosystem. This partnership is not just about sharing technology but also about a joint commitment to maintain and continually improve this integration. Getting started with langchain-huggingface is straightforward. Here’s how you can install and begin using the package: pip install langchain-huggingface #llm #huggingface #langchain #genai #generativeai #artificialintelligence

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Discover my new personal web site : Free prediction contest (without money) : Linkedin has delete my precedent post since he think betting : no no money free and respect country legislation ! https://lnkd.in/ea2AD6Dc Discover our new web site : >> Done > Place prediction > Register with captcha v2 login and connect with google. Front with https://bolt.new and backend from sratch in NodeJS. >Matchs : France ligue1 matchs are automatically injected in database via a free api in json >Admin module to manage users, tournaments bets matchs >Have a libretranslate docker to translate website more than 30 languages >forgot password >captcha front >>Incoming >See results scores >Add provisioning of all ligue provided by the free api : more than 100 >add top entity service ID code to manage yourself your tournament and invite your friends but quote calculation will stay global >Show Google analytics results called by API >Simple model for quote calculation first >See fictiv rewards. >captcha back >update Register with captcha Google v3 >Quote calculation with Monte Carlo Simulations >Improve speed translation by pre stored all sentence traducted Please test and give me your reviews. ^^ Article here : https://lnkd.in/egU_S3he Lien vers le site : https://lnkd.in/e3hiKVAP

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Predictive ML Model vs GenAI Model Un modèle de machine learning prédictif est un outil statistique ou algorithmique utilisé pour faire des prédictions sur des données futures ou inconnues en se basant sur des données historiques.  Fontionnement :  -       Collecte de Données -       Préparation des Données -       Sélection du Modèle -       Entraînement -       Évaluation -       Optimisation -       Déploiement -       Maintenance Un modèle de génération d'IA (GenAI), souvent appelé modèle de génération de texte ou modèle de génération d'images, est une catégorie de modèles d'intelligence artificielle conçus pour créer du contenu original à partir de données d'entrée. Ces modèles utilisent des techniques avancées de machine learning et d'apprentissage profond pour produire des résultats qui imitent ou complètent des données humaines, que ce soit sous forme de texte, d'images, de musique, ou d'autres formats. Fonctionnement -       Formation sur des Données Massives  -       Architecture du Modèle -       Encodage et Décodage -       Création de Contenu -       Affinage et Personnalisation

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[Must see] 🔥 👉 Come and discover the Fragrance of Deep Tech animation zone. #AI, #Optics, #Datascience, #Blockchain, #MachineLearning 𝐝𝐢𝐬𝐜𝐨𝐯𝐞𝐫 𝐡𝐨𝐰 𝐃𝐞𝐞𝐩 𝐓𝐞𝐜𝐡 𝐜𝐚𝐧 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦 𝐲𝐨𝐮𝐫 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬, 𝐟𝐫𝐨𝐦 𝐩𝐫𝐨𝐝𝐮𝐜𝐭 𝐝𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 𝐭𝐨 𝐦𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠, 𝐦𝐚𝐫𝐤𝐞𝐭𝐢𝐧𝐠, 𝐚𝐧𝐝 𝐝𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧! Île-de-France is a vibrant, rapidly growing region. With its high concentration of talent, capital, and business opportunities, is Europe's leading economic hub and a global force in R&D. The region is internationally recognized for the excellence of its health research and its dominance in several industrial sectors, including cosmetics. The cosmetics industry thrives in the Paris Region, covering the entire value chain—from product design and production to marketing and distribution. The region stands as a global center of excellence for beauty product innovation, attracting top companies and talent. Systematic-Paris-Region, Europe's premier Deep Tech competitiveness cluster, unites nearly 900 members. This includes over 550 start-ups, SMEs, and mid-sized companies, 140 major corporations, 160 R&D organizations, as well as a strong network of investors, institutional partners, and local authorities. 𝐓𝐨𝐠𝐞𝐭𝐡𝐞𝐫 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 Région Île-de-France , Systematic Paris-Region 𝐢𝐬 𝐬𝐡𝐨𝐰𝐜𝐚𝐬𝐢𝐧𝐠 𝐚𝐧 𝐢𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐭𝐫𝐚𝐢𝐥 𝐚𝐭 COSMETIC 360 𝐝𝐞𝐝𝐢𝐜𝐚𝐭𝐞𝐝 𝐭𝐨 𝐭𝐡𝐞 𝐜𝐨𝐬𝐦𝐞𝐭𝐢𝐜𝐬 𝐢𝐧𝐝𝐮𝐬𝐭𝐫𝐲, 𝐚𝐩𝐭𝐥𝐲 𝐧𝐚𝐦𝐞𝐝 "𝐓𝐡𝐞 𝐅𝐫𝐚𝐠𝐫𝐚𝐧𝐜𝐞 𝐨𝐟 𝐃𝐞𝐞𝐩 𝐓𝐞𝐜𝐡." 5 SMEs, all Systematic members and trailblazers in cutting-edge digital technologies, will present their 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, revolutionizing various industrial and business processes: AT BLOCKCHAIN SOFTWARE Deliver complete transparency to your consumers about the origin and naturalness of your products through the power of blockchain technology. Beink Dream While human creativity is unmatched, Beink Dream enhances it, enabling you to bring your visions to life at the speed of imagination. Datategy Revolutionizing the adoption of artificial intelligence and data science, DATATEGY helps business and technical teams effortlessly implement AI solutions through its intuitive and comprehensive platform, papAI. Scortex Enhancing manufacturing quality with a platform that digitizes quality control—intelligently automating visual inspections and providing real-time analysis. Tower Farm Growing high-quality, hyper-local botanical products, harnessing nature and technology for pharmaceutical, cosmetic, and nutraceutical applications. We look forward to welcoming you to our ‘Deep Tech’ zone!

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In case you missed it, last month my approach to the SLB #SemanticSegmentation challenge for defect detection on Challenge Data platform by Ecole normale supérieure and Collège de France proved to be the winning one at the mid-season breakdown. If you think making CNNs is too complicated for you and you shy away from embarking on the #DeepLearning journey, like I did 4 months ago, let me try to persuade you otherwise. On my website 🔽, I have shared what I learned along the way - approaches towards data preprocessing, network architecture and training that allowed me to score 0.67 IoU for the challenge of labelling defects on ultrasound images. It was a learning process and it still is, so if you have any questions or remarks, hit me up and let’s grow together. 🌳 (Credits: U-Net architecture diagram; Long, Shelhamer, and Darrell, 2014)

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⭐️ New video release 📺: I achieved peak performance in python, here's how ... Watch Dishant Sethi as he reveals the secrets to achieving peak performance in Python and optimizing your code for speed and efficiency. 📺 Watch the video on YouTube: https://lnkd.in/e9pS54Hg Dishant Sethi, Founder at prodinit.com, a software consultancy, shared insights on optimizing Python code for peak performance. The talk focused on techniques to enhance speed and reduce resource consumption, emphasizing memory efficiency. Key points included optimizing functional execution, rigorous testing, and performance enhancement. Common bottlenecks like inefficient coding practices, memory leaks, and suboptimal algorithms were discussed. Strategies for profiling code and achieving peak performance in data-driven applications were also explored, such as efficient DataFrame storage and looping techniques. Attendees gained valuable insights regardless of their experience level, with practical takeaways to improve Python code efficiency. By understanding the principles behind efficient Python coding, developers can enhance their optimization skills.

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⏳ 1 week left! 🎲 The day on probabilities in theoretical computer science is coming up. Our last speaker is Adeline Roux-Langlois from Greyc Caen. Her talk will be on "𝐔𝐬𝐢𝐧𝐠 𝐑𝐞́𝐧𝐲𝐢 𝐝𝐢𝐯𝐞𝐫𝐠𝐞𝐧𝐜𝐞 𝐢𝐧 𝐥𝐚𝐭𝐭𝐢𝐜𝐞-𝐛𝐚𝐬𝐞𝐝 𝐜𝐫𝐲𝐩𝐭𝐨𝐠𝐫𝐚𝐩𝐡𝐲". Abstract: Lattice-based cryptography is a very interesting candidate to obtain constructions which resist attacks using a quantum computer. Even if powerful enough quantum computers do not exist yet, it is necessary to be prepared and anticipate their arrival. In this talk, I will introduce lattice-based cryptography and some use of the Rényi divergence in the security proofs. Security proofs allow to prove the security of a cryptographic construction by showing that attacking the scheme is at least as hard as solving a difficult algorithmic problem. 🔗 There's still time to register! ➡ https://lnkd.in/eUabuc3h

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Interesting and the best part is that in the comment section, people share other useful packages other than pipreqs, such as pip-tool, Poetry, uv and others.

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📢 Exciting News! Our final lecture in the "Python for Data Science" course is now live! 🎉 In this session, we tackle our capstone project on predicting the price of pre-owned cars using linear regression and random forest regression models. Here’s what we cover: 🔍 **Topics:** - Handling missing values and data cleaning - Creating a new variable to determine the age of each car - Analyzing distributions and correlations of key numerical variables - Building and evaluating linear regression and random forest regression models - Comparing model performance using RMSE and R-squared metrics We show you the practical steps to deal with real-world data science challenges and demonstrate how the random forest model outperforms linear regression, especially with imputed data. This lecture is packed with valuable insights and hands-on techniques to enhance your data science skills. Don't miss out! 🔗 https://lnkd.in/gZ5eB5jf 📊 Data Science, Python Programming, Machine Learning, Regression Models, Linear Regression, Random Forest, Data Cleaning, Data Imputation, Predictive Modeling, Capstone Project 🔖 #DataScience, #PythonProgramming, #MachineLearning, #RegressionModels, #LinearRegression, #RandomForest, #DataCleaning, #PredictiveModeling, #CapstoneProject 👍 If you found this course helpful, please like, share, and follow us for more data science content. Thank you for being part of our learning journey! 🚀

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Everyone is diving into learning about LLMs, and this is great because it's leading to the creation of exceptional resources. . . . #mlops #ai #LanguageModel #MachineLearning #DeepLearning #ArtificialIntelligence #TechInnovation #LLM

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[11/42] My second project idea for @_buildspace : USE AI TO SOLVE MATH QUESTIONS EFFECTIVELY The current AI models are not very effective in solving difficult Math problems, They are not up to 90% accuracy. We want to build this. Excited about the challenge! Please, you can give your feedback below: @FarzaTV @_nightsandweekends

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Le gratin du machine learning...

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 Exciting AI Research Alert: Balancing Datasets for Better Models 🧠 A team of researchers from Meta, INRIA, Université Paris Saclay, and Google have developed an innovative technique to automatically balance large datasets. This is crucial for improving AI model performance across all categories. Key points: Uses iterative k-means clustering to create balanced subsets Applied to massive image (743M) and text (210B tokens) datasets Models trained on balanced data outperformed those on unbalanced data 🔑 Why it matters: Improves model fairness and performance Saves time and resources in manual data balancing Applicable to both vision and language models This research highlights the ongoing importance of data-centric AI approaches, even in the era of foundation models. What are your thoughts on this technique? How might it impact your AI projects? #ArtificialIntelligence #MachineLearning #DataScience #AIResearch

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Hi, I just created a video about CNN (Convolutional Neural Networks). In this video, I explain the basic concepts used in CNNs, like convolutions and pooling. I also show some code at the end that trains a model to recognize handwritten digits. Check it out here : https://lnkd.in/dTGB9Htu #CNN #ConvolutionalNeuralNetworks #pooling #convolutions

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