Q42’s Post

Starting my journey from Software Engineering to the AI Galaxy Excited to dive into the world of AI with this great article: "Machine Learning for Everyone". Read here: https://lnkd.in/eaiFsqfc Let’s bridge the gap between complex theory and practical innovation! #AI #MachineLearning #DataScience #SoftwareEngineering #ArtificialIntelligence #TechJourney #LifelongLearning #Innovation #SystemRecommendation #ELearning #DistanceLearning #KnowledgeGraph

Unlocking Machine Learning: Engineering Practical Solutions Discover how machine learning engineers transform algorithms into scalable systems for real-world applications. We explore the architect-like role they play, designing robust frameworks that process data efficiently and deliver reliable results. Join us to dive into the practical side of machine learning engineering! See the full video: https://lnkd.in/guagSZsX #MachineLearning #DataScience #EngineeringSolutions #TechInnovation #AI #DeepLearning #MLEngineering #DataProcessing #AlgorithmDesign #RealWorldApplications

" Potential of Artificial Intelligence (AI) and Machine Learning (ML)! As we continue to navigate the rapidly evolving landscape of AI and ML, it's exciting to think about the endless possibilities these technologies present. From revolutionizing healthcare and finance to transforming customer experiences and streamlining operations, AI and ML are redefining industries. What are some of the most innovative AI/ML applications you've come across? Share your thoughts! #Artificial intelligence #Machine learning #Innovation #Digital transformation #Future of work - #Air evolution - #Ml algorithms - #Deep learning - #Processing - #Computer vision - #Robotics - #Automation - #Data science

Understanding the Difference: AI vs. Machine Learning In this 2-minute excerpt from our recent masterclass, Olamide Jolaoso CRB explains how machine learning enables systems to learn and improve from data without explicit programming. From predicting customer behavior to enhancing business decisions, the applications are vast! Watch the full masterclass on YouTube for a deeper dive into Data Analytics and AI. 🔗 [https://lnkd.in/eFAKbCdK] #DataAnalytics #ArtificialIntelligence #MachineLearning #ProfessionalDevelopment #TechInsights

The Role of Feature Engineering in Machine Learning Feature Engineering is the process of creating new input features or transforming existing ones to improve the performance of machine learning models. It plays a vital role in helping models better understand the data, resulting in more accurate predictions. Key techniques in feature engineering include: Handling missing data (e.g., filling missing values with mean/median). Scaling features (e.g., normalizing or standardizing data). Encoding categorical variables (e.g., converting categories into numerical values using techniques like one-hot encoding). By transforming raw data into meaningful inputs, feature engineering helps models achieve better performance without necessarily changing the algorithm. #machinelearning #veltria #ai #dailycontent

🌟 Exciting Announcement: Introducing Teachable Machine by Google! 🌟 I am thrilled to share with you a groundbreaking tool that is revolutionizing the way we interact with AI: Google’s Teachable Machine! 🚀 In this video, I walk you through the incredible capabilities of Teachable Machine, a web-based platform that allows anyone to explore machine learning in a hands-on, interactive way. Whether you’re a seasoned developer or completely new to AI, this tool empowers you to create your own models. 🎥 Watch the full video to learn more about Teachable Machine and its endless possibilities.! #AI #MachineLearning #TechInnovation #Google #TeachableMachine #LinkedInLearning #AIMER Society - Artificial Intelligence Medical and Engineering Researchers Society #AIMERS #APSCHE

💡 Inference Time in Generative AI: The Next Frontier 💡 For decades, people have compared computer science to brains. But in the Generative AI world, we’re finally digging into what it means to build systems that don’t just compute—they reason. Lately, I’ve been obsessed with how inference time plays into this. Three standout innovations have been keeping me up at night (in the best way): OpenAI o1 Reasoning Model: Dropped in September, this model uses long internal chains of thought to tackle complex problems by “thinking” before answering. It’s crushing benchmarks in programming and science tasks, proving that deliberate reasoning paired with test-time compute is a game-changer. https://lnkd.in/ej4m2a83 MIT’s TTT Framework: This paper hit earlier this month and really nailed how test-time training can push models toward abstract thinking. It bridges the gap between training and deployment, letting AI refine its reasoning dynamically and in real time. https://lnkd.in/e8udUpxC LLaVA-01: Released on November 15, 2024, this is the first open-source reasoning model (that I’m aware of) to also integrate image generation. Its structured, multi-stage reasoning process combines summarization, visual interpretation, logical reasoning, and conclusion generation to tackle complex visual tasks. Even with just 100,000 training samples, it outperforms larger models like Gemini-1.5-pro and GPT-4o-mini on multimodal reasoning benchmarks. https://lnkd.in/eDbR_6Mn These models are proof we’re moving into an era where AI doesn’t just spit out answers—it reasons its way there. As inference time becomes a bigger part of the equation, I’m hyped to see what new applications will come out of this. What use cases are you excited about? Let’s chat! #AI #GenerativeAI #ReasoningModels #InferenceTime #Innovation

Exploring the journey of building machine learning models! From problem identification to evaluation, each step is crucial in creating impactful AI solutions. A glimpse of the Machine Learning Model Life Cycle. #MachineLearning #DataScience #ArtificialIntelligence #MLLifeCycle #AIInnovation #AIML

Hi All 🤠 , 🤩 I came across a free course on Generative AI🌈, taught by the experts at Google and Kaggle! 🤩 Here’s what each day of the course will cover: 🔌 Day 1: 🤖 Foundational models & prompt engineering 🗣 - from transformers to prompt engineering techniques 🌐 Day 2: 🤔 Embeddings & Vector databases 📡 - deep dive into embeddings and vector search applications 🔗 Day 3: 🤖 Generative AI Agents 💬 - building sophisticated agents step-by-step 🔬 Day 4: 💉 Domain-specific LLMs 🧬 - insights into models like SecLM and Med-PaLM 🔧 Day 5: MLOps for GenAI - practical MLOps practices with Vertex AI 🏭 This is a great opportunity for anyone curious about AI, with no prior knowledge needed! I just have an eagerness to learn, a Kaggle account, access to AI Studio, and lots of curiosity! 😄 Check it out here: https://lnkd.in/dWzR_xXh ⏳ Timings: 11th-15th Nov 2024 at 11:00 am ET / 4:00 pm GMT/ 9:30 pm IST. Let's learn together!!! #LearnMoreShareMore #AI #ML #MLOps #Kaggle #Google #GenAI #LLMs #promptengineering #AIAgent #vectorstore #embeddings #vectordb #notebooklm #NotebookLM Kaggle Google

Ready to explore #AI’s impact on finance? Join Forvis Mazars US for an informative webinar covering everything from machine learning to the AI life cycle and beyond. Learn about prompt engineering, supervised and unsupervised learning, and practical use cases. Sign up now: https://lnkd.in/eE79Dqs9