In case you missed our news from yesterday, FedLearn has launched a new micro-course, Difference Between Discriminative & Generative AI Models (GENAI104). The course was developed using publicly available content from Defense Acquisition University and is the third in our new GENAI learning path. Explore our artificial intelligence and data catalog and purchase the course today: https://lnkd.in/eJQBfqCA #fedlearn #departmentofdefense #dod #govcon #trainingprovider #traininganddevelopment #training #onlinelearning #onlinecourses #asynchronous #upskilling #artificialintelligence #ai #aitraining #largelanguagemodels #LLMs #generativeai #genai
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Saturday readings: The hidden capabilities of #genAI Its not a stochastic ?? after all? Interesting study around the so called concept space. Key question is to explore if the model can generate images it has never seen before, I.e learning concepts and not just memorizing TL;TR: most likely the models KNOW much more, but are not aware about it. And we as users do not know how to properly extract and instruct them to do so https://lnkd.in/gibvqMEq #concept #learning
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YouTube is free education. But 98% don't know the best professors. Here are the top 6 channels to accelerate your learning: 1/ DeepLearningAI DeepLearning AI has become the pathway for anyone looking to build an AI career. https://lnkd.in/dsmK2WAC 2. Notion Notion tutorials and other deep dives into productivity tools with Thomas Frank. https://lnkd.in/dC8hS5J8 3. MIT OpenCourseWare Get free access to MIT's official courses. Dive into top-notch learning at your own pace. https://lnkd.in/d5KrrwX2 4. Simplilearn World’s #1 online Bootcamp and one of the world’s leading certification training providers. https://lnkd.in/dYQ49zkb 5. Prompt Engineering Learn about different LLMs and advanced prompting techniques. https://lnkd.in/dv3my9C6 6. Matt Wolfe Covers AI, no-code, and futurism. Shares news, reviews tools, and discusses tech's future. https://lnkd.in/eERwkP5N #Artificial #Intelligence #Deep #Learning #Tutorial #Tools #Videos
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Data Science and AI Master's Student, Software Engineer, Native Android Developer
?? New article alert! I just dropped a beginner-friendly guide on Ensemble Learning—a powerful technique to make machine learning models smarter and more accurate. If you've ever wondered will combining multiple models boost performance, this one's for you! Check it out here ?? https://lnkd.in/eGUDBvAs Let me know your thoughts, and feel free to share any feedback! #MachineLearning #DataScience #DeepLearning #EnsembleLearning #AI
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Day 2 of our AI Learning Advent Calendar??? ???History of AI: Key Milestones in AI Development??? Artificial Intelligence (AI) has made tremendous strides in recent years, with 2024 marking a particularly significant year in its development. One of the most notable milestones was the advancement in generative AI, where models became better at creating high-quality content, including text, images, and even videos, with impressive accuracy and creativity. This has opened up new possibilities for content creation across various industries, from media and entertainment to marketing and education. ???? Moreover, the year saw significant progress in autonomous systems, with AI-powered drones and self-driving vehicles becoming more reliable and widely adopted. These advancements have the potential to revolutionize transportation, logistics, and many other sectors, enhancing safety and efficiency. ???? Be prepared to master the newest versions of AI and harness their capabilities by starting our dedicated learning path: https://lnkd.in/gfsjzbvU ?? As we reflect on these milestones, it's clear that AI is not just a technological trend but a fundamental shift shaping our future. Let's continue to explore and harness the power of AI to drive innovation and make a positive impact on our world. ??? Catalin Tataran David Cervigón Luna Irina Bors , Lurlene Duggan Jenny L?ngstr?m Marja Aho , Britt Mc Carthy Mors Eyyuphan Keskin Simon Sparrow Anneleen ‘AV’ Vaandrager Stéphanie Straub Mariangela Orme , Vania Neto, Paul Griffiths Bora Kivrak, Paul Chiola Pénélope Brouchon , Füsun Ovat, Jane Pitt Layal Azar, Michael Mansour , Laetitia Barbé-Ozouf #MSadvocate #mslearn #azureai #aifundametals #aihistory #Innovation
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Faculty of Artificial Intelligence || instructor at Techno Kids & Techno Future || instructor at smart technology || instructor at future kids ||
?? Exciting News! ?? ?? I'm thrilled to share my completion of the "Machine Learning in Production" course by DeepLearning.AI, accredited with 4.8 stars from 2,902 ratings and boasting an enrollment of over 110K students on Coursera! This course has been an incredible journey, equipping me with essential skills for navigating the complex landscape of machine learning deployment and production. Here’s what I've gained: ?? Understanding the ML Project Lifecycle: I now grasp the key components and stages involved in bringing machine learning models from development to deployment in real-world scenarios. ?? Optimal Model Performance: I've learned to prioritize key slices of data that disproportionately influence model metrics, ensuring our models perform robustly across various scenarios. ??? Tackling Production Challenges: Whether it's structured, unstructured, small, or big data, I am now equipped to handle the nuances and challenges of each, ensuring smooth deployment and maintenance. ?? Importance of Label Consistency: I've gained insights into the criticality of consistent labeling in training data, crucial for model accuracy and reliability. This experience has not only expanded my technical proficiency but also reinforced the importance of continuous learning and adaptation in the dynamic field of AI and machine learning. #MachineLearning #DeepLearningAI #ProductionReady #DataScience #ContinuousLearning #AIDeployment 3.5
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Interested in learning about a new 'Big' paradigm for vision foundation models?We are excited to host the 1st edition of our tutorial "Time is Precious: Self-Supervised Learning Beyond Images" with Mohammadreza Salehi and Yuki Asano on Monday 30th of September at #ECCV2024 from 09:00 to 13:00 at Amber 7+8. More information: https://lnkd.in/dq9BdkM2 We also have an exciting line of speakers - Ishan Misra, Jo?o Carreira and Emin Orhan, who will share their wonderful insights into leveraging video pretraining for different applications. Quick Summary of our tutorial: The primary goal of this tutorial is to introduce to the computer vision community, the concept of learning robust representations by leveraging the rich information in video frames. While, image-based pretraining has gained recent popularity with SimCLR, the practice of pretraining models from videos dates back much earlier. This tutorial will recapitulate both early and recent works, which have pretrained image encoders using videos for different pretext tasks such as egomotion prediction, active recognition, dense prediction etc. We also discuss practical implementation details relevant for practitioners and highlight connections to other, existing works such as VITO, TimeTuning, DoRA, V-JEPA etc. We also discuss recent works aimed to mimic human visual systems such learning from one continuous video stream and by learning from longitudinal audio-visual headcam recordings from young children, thereby putting this concept into a broader context. Key questions we aim to tackle in this tutorial include: - Can we learn strong image encoders from good quality videos (i.e. with limited data)? - Do we need synthetic augmentations? How useful are the natural augmentations in videos? - Can we learn from a continuous stream similar to humans?
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In this tutorial, we will show how time can be effectively leveraged as a rich supervisory signal for self-supervised learning methods. The tutorial is relevant not only to researchers focused on self-supervised learning but also to those applying these techniques to specific domains, such as robotics and embodied AI. Join us to hear from an exciting lineup of speakers as they discuss the next generation of self-supervised learning techniques.
Interested in learning about a new 'Big' paradigm for vision foundation models?We are excited to host the 1st edition of our tutorial "Time is Precious: Self-Supervised Learning Beyond Images" with Mohammadreza Salehi and Yuki Asano on Monday 30th of September at #ECCV2024 from 09:00 to 13:00 at Amber 7+8. More information: https://lnkd.in/dq9BdkM2 We also have an exciting line of speakers - Ishan Misra, Jo?o Carreira and Emin Orhan, who will share their wonderful insights into leveraging video pretraining for different applications. Quick Summary of our tutorial: The primary goal of this tutorial is to introduce to the computer vision community, the concept of learning robust representations by leveraging the rich information in video frames. While, image-based pretraining has gained recent popularity with SimCLR, the practice of pretraining models from videos dates back much earlier. This tutorial will recapitulate both early and recent works, which have pretrained image encoders using videos for different pretext tasks such as egomotion prediction, active recognition, dense prediction etc. We also discuss practical implementation details relevant for practitioners and highlight connections to other, existing works such as VITO, TimeTuning, DoRA, V-JEPA etc. We also discuss recent works aimed to mimic human visual systems such learning from one continuous video stream and by learning from longitudinal audio-visual headcam recordings from young children, thereby putting this concept into a broader context. Key questions we aim to tackle in this tutorial include: - Can we learn strong image encoders from good quality videos (i.e. with limited data)? - Do we need synthetic augmentations? How useful are the natural augmentations in videos? - Can we learn from a continuous stream similar to humans?
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The Applied Machine Learning course by IAT-Digital is a fantastic program for anyone looking to deepen their understanding of machine learning concepts. Sunny Verma, PhD, brought the sessions to life with real-world examples, making complex topics like confusion matrices and evaluation metrics approachable and practical. His interactive teaching style provided valuable insights into the ML lifecycle and project methodologies. This program, offered by TAFE NSW and the Institute of Applied Technology, stands out for its rich content, tutor-led sessions, and hands-on projects designed to bridge the gap between theory and application. Additionally, the inclusion of Microsoft Certification at no additional cost—adds immense value for participants. A highly recommended course for those seeking a comprehensive and engaging learning experience in machine learning! ?? #MachineLearning #TAFENSW #AI #IATDigital
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Co-Founder of Altrosyn and DIrector at CDTECH | Inventor | Manufacturer
4 个月Developing accessible training materials on complex AI topics like discriminative and generative models is a commendable goal. It's encouraging to see initiatives like FedLearn bridging the gap between technical expertise and public understanding. What specific challenges did you encounter when adapting Defense Acquisition University content for a wider audience?