Momenta’s Post

Hello Everyone, Please join us for a LIVE demo! "Climb under the hood" and learn how to build, train, and publish an #AI #agent with the Composabl Platform! Register here ➡️ https://lnkd.in/gaFjjNAA Experience what it's like to build an AI agent that makes million-dollar decisions 13% better than the best industry benchmarks. Walk away knowing how to: -Design multiple agents for a real industrial use case -Use your own Python algorithms and machine learning models in Composabl  -Train agents to perform the high-value tasks you need -Analyze agent behavior -Deploy trained agents to control real equipment PS: If you registered for the Nov 14th session great, do not be confused! That session is full hence this new session ;) #MachineTeaching #MachineLearning #AutonomousAI #Industry40 #Automation #SmartManufacturing #AIforIndustry  

👉 Learn how to build, train, and publish an AI agent. Not just your regular intro. Will you build a million-dollar decision AI-agent?

🚀 Ray cluster's autoscaling feature is now supported on Ray on Vertex AI! 🚀 💥 Ray on Vertex AI is one of the most exciting and growing services for scaling AI workloads on Vertex AI. With Ray's new autoscaling feature, your clusters now dynamically adapt to your AI workload demands 💪 🕹️ How it works Thanks to the Vertex AI Python SDK, you simply define the autoscaling configuration by specifying the minimum and maximum replica count of a worker pool. 💡 Tip: Choose between autoscaling for hands-off optimization or manual scaling for granular control. Check out the documentation in the comments 👇 to know more about scaling Ray clusters on Vertex AI! And if you find this post helpful 🔥, like, share and let’s connect if you have questions 🤗 #GoogleCloud #VertexAI #Ray #Autoscaling #MachineLearning #AI #DataScience

🌟 Day 10 of #100DaysOfComputerVision: Data Processing for AI Virtual Mouse 🌟 Excited to share my progress on this week’s project — an AI Virtual Mouse 🖱️ powered by OpenCV and Python! 🎯 Today’s Focus: Data Processing Data is the foundation of any AI project, and here's how I’m handling it: 🔹 Frame Capture: I’m using my webcam to capture real-time frames. Each frame acts as an image, which we can later process for gesture recognition. 🔹 Preprocessing: Raw frames are noisy, so I’ve implemented several techniques to clean and standardize the input: Resizing: Ensures that every frame has consistent dimensions for accurate prediction. Grayscale Conversion: Reduces computational load by focusing on essential visual information. Gaussian Blur: Applied to smoothen the image, helping to reduce noise and emphasize gesture shapes. 🔹 Hand Detection: I’m using skin color detection to isolate the hand region. This step is critical for recognizing different gestures that control the mouse! 💡 Next Steps: Tune parameters for more accurate detection. Implement finger tracking and gesture-based controls for mouse movement, clicking, and scrolling! 🚀 What's next? I’ll soon dive into gesture recognition and the fun part—making the mouse move and click based on hand gestures! Stay tuned for the updates! 🔥 #OpenCV #Python #ComputerVision #AI #MachineLearning #DeepLearning #DataProcessing #AIProject #LinkedInChallenge #TechJourney #Innovation

🌟 Today's Deep Dive: Blurring & Smoothing with OpenCV🔍 Just wrapped up an exciting session on blurring and smoothing techniques in OpenCV! Here’s a quick breakdown of what I learned: 🔲 Kernel/Window: A kernel is a matrix used to apply filters on an image. It’s essential in smoothing techniques as it defines the area of pixels used to calculate the blur. 📏 Kernel Size: The size of the kernel (e.g., 3x3, 5x5) directly affects the blur intensity—larger kernels result in smoother blurs but can also reduce image sharpness. ⚙️ Average Blur: This method takes the average of all pixel values in the kernel area, resulting in a simple, even blur. Ideal for reducing noise in an image. 🔵 Gaussian Blur: Instead of a flat average, Gaussian blur uses a weighted kernel, with higher weights given to pixels closer to the center, creating a more natural and soft blur. Commonly used in image preprocessing. 🔴 Median Blur: Unlike average blur, the median value of the pixels is used, which helps to remove salt-and-pepper noise while preserving edges—perfect for noisy images! 🔍 Bilateral Blur: The most sophisticated of the bunch! It smoothens an image while preserving its edges, making it ideal for maintaining sharp boundaries in images. These techniques are crucial in computer vision for tasks like image preprocessing, noise reduction, and preparing data for advanced analysis. Can’t wait to see how this knowledge enhances my future projects! #OpenCV #ComputerVision #ImageProcessing #Blurring #Smoothing #GaussianBlur #MedianBlur #BilateralFilter #TechLearning #MachineLearning #AI #DeepLearning #DataScience #TechInnovation #Python #Robotics #AutonomousSystems

Access to AI Marathon Webinars is Open to Everyone! If you missed the course registration, you can still watch the recorded lectures. The topics covered include: 🔸 Artificial Intelligence: Past, Present, Future 🔸 The Future of Automotive Mobility 🔸 What Makes AI Understand Us 🔸 Create the Chatbot on your computer: Running Python Scripts in Google 🔸 Colab 🔸 Chatbot building: Live coding 🔸 Ethical Considerations in AI We have also prepared useful resources for studying the topic of artificial intelligence: Useful AI Study Resources. Enjoy the learning!   Link: https://lnkd.in/geqr9HYv

#NeuralNetworks 👩🏻💻👩🏻💻 For one of my recent projects, I've fine-tuned neural networks using various optimizers (Adam, SGD, SMOTE). Thank you Ricardo Isaac Hernández Cruz for your guidance on my #Python journey. For taking the time to explain to me when I need it and for sharing best practices. It’s cool to have the opportunity to complement each other’s strengths by sharing business & coding knowledge. When it comes to #PairProgramming with #Copilot, it’s like having a trusty co-pilot by your side. Here’s the deal: understand your business context. Copilot isn’t autopilot; it’s a collaborative tool. And for #NN, choosing the right number of neurons is where context matters. Highly recommend using #Copilot ⭐️ #Python #MachineLearning #AI #ArtificialIntelligence

🚀 Excited to share my latest project: Mukhparichay – a Python-powered attendance system! 🎉 Using NumPy, OpenCV, DateTime, and more, I built an intuitive solution that simplifies attendance tracking with facial recognition. 📸 Here's how it works: When a user stands in front of the camera, Mukhparichay checks if their photo exists in the system. If recognized, their attendance is instantly marked ✅. If not recognized, it identifies an "Unknown User" and prompts the entry of new student details, integrating them into the system on the spot! 🙌 It’s efficient, scalable, and an exciting blend of AI and automation. 💡 Looking forward to exploring more and improving this project! Would love to hear your thoughts and feedback. #Python #AI #OpenCV #FaceRecognition #AttendanceSystem #Project #Automation #TechInnovation #LearningByDoing

AI generated picture | prompts: Illustrate A Sketch Describing A Workflow From Science To Production With Regard To Remote Sensing 𝐓𝐚𝐦𝐚 𝐆𝐫𝐨𝐮𝐩 𝐞𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 𝐔𝐬𝐞𝐫 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐨𝐧 𝐨𝐧 𝐀𝐩𝐫𝐢𝐥 17, 2024 𝐅𝐫𝐨𝐦 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐭𝐨 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐞𝐂𝐨𝐠𝐧𝐢𝐭𝐢𝐨𝐧 – 𝐌𝐞𝐭𝐡𝐨𝐝𝐬, 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬, 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 We are pleased to invite you to another Tama Group eCognition User Conference at our training center in Graefelfing near Munich on April 17th, shortly after the release of eCognition 10.4. The focus of the conference will be on three main topics:  𝐈. 𝐍𝐞𝐰𝐬 𝘦𝘊𝘰𝘨𝘯𝘪𝘵𝘪𝘰𝘯 10.4 𝐊𝐡𝐫𝐲𝐬𝐭𝐲𝐧𝐚 𝐁𝐞𝐳𝐛𝐨𝐫𝐨𝐝𝐨𝐯𝐚, 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 | 𝐓𝐫𝐢𝐦𝐛𝐥𝐞 provides insights into the new features of eCognition 10.4. * Visualize ‘Regions’ * Python Integration * Mobile Mapping and much more   𝐈𝐈. 𝐅𝐮𝐭𝐮𝐫𝐞 𝘙𝘦𝘥𝘦𝘧𝘪𝘯𝘪𝘯𝘨 𝘎𝘦𝘰𝘴𝘱𝘢𝘵𝘪𝘢𝘭 𝘍𝘳𝘰𝘯𝘵𝘪𝘦𝘳𝘴 𝘸𝘪𝘵𝘩 𝘈𝘐 We gain insights into the future with 𝐊𝐚𝐫𝐨𝐥𝐢𝐢𝐧𝐚 𝐓𝐨𝐫𝐭𝐢𝐥𝐥𝐚, 𝐃𝐢𝐫𝐞𝐜𝐭𝐨𝐫 𝐨𝐟 𝐀𝐈 | 𝐓𝐫𝐢𝐦𝐛𝐥𝐞 𝐀𝐈 “In the quest to maintain a digital model of the earth, AI slices through the bottleneck of data overload with unparalleled precision. From scene understanding to the creation of asset inventories, and condition analysis – the landscape of geospatial analysis is being redefined by advances in 3D algorithms and multimodal AI foundation models.”   𝐈𝐈𝐈. 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 𝘗𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴 Application and solutions from various areas * City of Munich and Singel-Tree Detection * Tama Group and Mobile Mapping and much more   We are excited to discuss these topics with you at the conference. If you would like to share your experiences with eCognition, please contact us. We look forward to your presentation. Participation in our eCognition user conference in Gräfelfing is free. A nearby restaurant is available for lunch during the break. If you cannot attend in person, you can join the meeting live via GoToMeeting. Click here for more Information about our eCognition User Conference 2024 https://lnkd.in/d_EBSid9   #eCogniton10.4 #geospatial #AI #trimble #mobilemapping #python

📌What is my favorite project to work on and why are they ALWAYS Computer Vision projects?🧚🏽♀️ 🎁My bias probably lies in the fact that the deeper I delve into Computer Vision, the more powerful I realize it is. In every project, there's always a point I go, "That's actually possible???" 🎁Another reason is how fun it is to experiment with python modules and see which ones could be incorporated in the project. 🎁Thanks to modules like opencv and numpy, seamless communication between modules like pyautogui, pycaw, pyaudio, mediapipe and vosk is possible. 🎁This GESTURE-CONTROLLER-SYSTEM project consists of four functionalities. 🖋️Volume Adjustment(Mode 1) 🖋️Brightness Control (Mode 2) 🖋️Screenshot Mode (Mode 3) 🖋️Audio Recording and Transcription(Mode 4) 🎁The working principles amd usage instructions for each mode is explained in the code folder's README file (https://lnkd.in/dXJHaRG8) 📌I'd love to improve this project in two ways. 🖋️Sound-Based Control: Replace gesture-based control with sound-based control, allowing users to interact with the system using specific sounds or voice commands. 🖋️Machine Learning-Based Gesture Detection: Utilize machine learning algorithms to detect gestures from any distance, angle, lighting condition, or background, improving accuracy and robustness beyond traditional hand landmark-based detection. #ai #machinelearning #computervision #mediapipe