Basler AG’s Post

Have you ever wondered how machines can recognize hand gestures? Hand gesture recognition technology can revolutionize human-computer interaction, making it more intuitive and seamless. Imagine controlling your devices with just a wave of your hand! Good to share my recent project on Hand Gesture Recognition using deep learning, developed as task 4 at Prodigy InfoTech! Here are some key highlights: Dataset: Utilized the comprehensive LeapGestRecog dataset from Kaggle, featuring over 20,000 images of various hand gestures. Data Augmentation: Implemented transformations including 10-degree random rotations, horizontal flips, and center cropping to enhance model robustness. Model Architecture: Designed a Convolutional Neural Network (CNN) with 3 convolutional layers, each followed by batch normalization and max pooling, culminating in a fully connected layer. Training: Achieved an accuracy of 95% on the validation set after training the model for 30 epochs with a batch size of 64. Performance: Leveraged PyTorch for model development, resulting in efficient training and real-time inference capabilities. If needed you can check the GitHub repository here :https://lnkd.in/gapGqR3F Excited to apply this technology to innovate gesture-based control systems! 🌟 #MachineLearning #DeepLearning #AI #ComputerVision #HandGestureRecognition #PyTorch #Kaggle #ProdigyInfotech

A few years back, computer vision was a field of research, considered complex and resource-intensive. Today, open source has democratized it, making advanced features accessible to engineers for commercial usage. Facial recognition accuracy has significantly improved. Behavioral analysis with a combination of opencv output and open source NLP libraries is mind-blowing. Processing video data with computer vision required huge compute power, it still does but compute itself is widely available & easily accessible now. Running on GPUs to process huge volume of data is a mundane exercise and even real time analysis with negligible latency is run of the mill stuff. 📈 Customers' default expectations continue to increase. What was cutting edge/advanced tech 3 years back is going to be a hygiene factor now. #ComputerVision #OpenSource #AI #FacialRecognition #BehavioralAnalysis #RealTimeAnalysis

Yes, AI is a tool, NOT God. Use it wisely, NOT worship it blindly. My AI initiative is: "With my intellectual property (IP), I can do what ChatGPT, an icon of AI, can't do in data analytics." Do you or any of your contacts need my expertise and my IP? I am selling, NOT just talking. Thanks. P.S. I wrote this statement myself, without using any AI tool.

🚀 Exploring the Sliding Window Technique in Object Detection 🚀 I’ve been diving into object detection recently, and one of the foundational techniques that caught my interest is the Sliding Window approach! It’s an older but essential method that’s helped shape the field of computer vision. 🔍 How It Works Imagine scanning an image with a magnifying glass, moving it section by section. The sliding window technique does just that: it systematically checks each part of an image for the object we’re searching for, resizing and sliding as it goes. While it’s computationally heavy, it ensures a thorough search of all areas. ⚖️ Why It Matters Pros: Great for exhaustive searches. It doesn’t miss a spot! Cons: Computationally expensive and can repeat itself, which can slow things down, especially in deep learning models. 🤔Modern Alternatives Newer methods like YOLO and SSD have made object detection faster and more efficient, reducing the need for sliding windows. But it’s always interesting to understand these foundational techniques and see how far we’ve come in AI! #ComputerVision #ObjectDetection #MachineLearning #DeepLearning #AI

🔍 What’s holding you back from diving into AI as a Product Manager? If you’re ready to future-proof your skillset, NVIDIA offers five free courses to get you started! Here’s a peek: 1. Accelerate Data Science: - Unified CPU & GPU workflows - Speed up processing times without changing code 2. Build RAG Agents with LLMs: - Scalable deployment & vector databases - Use LangChain for dialogue and retrieval 3. Augment LLM with RAG: - RAG basics and retrieval for deeper insights 4. Build a Brain in 10 Minutes - Neural networks and data insights 5. Generative AI Explained - Industry applications and mechanics behind Generative AI - Challenges and potential of Generative AI Curious? Check the link in the comments to start learning! 🚀 Tag someone who needs this! 👇 #Upskilling #ProductManagement #AI #ProfessionalDevelopment #LearningJourney #CareerGrowth #ProductAnalytics #MicroCertification #ProductStrategy #AIforProductManagers #FutureSkills

🚀 Optimizing Large Language Models: Diving into Quantization for Efficiency and Performance Today, I focused on the fascinating realm of quantization, exploring both symmetric and asymmetric techniques. In the ever-evolving world of AI, fine-tuning large language models (LLMs) presents both exciting opportunities and significant challenges, particularly around computational costs and resource requirements. One promising solution is quantization, a technique designed to make these massive models more efficient by reducing the precision of their data. 💡 Real-World Example: The LLaMA3.1–70B model with FP32 precision requires a staggering 336 GB VRAM, making inference feasible only with multiple high-end GPUs. But with 4-bit quantization, the memory footprint reduces by ~90% to just 42 GB, enabling efficient deployment on a single A100 GPU. This demonstrates quantization's transformative potential in democratizing LLM accessibility. What is Linear Quantization? Linear quantization is one of the most widely adopted methods for compressing LLMs, mapping model weights from higher precision (e.g., FP32) to lower precision (e.g., FP16, BF16, INT8). 🔑 Two Main Modes: 1️⃣ Asymmetric Linear Quantization: Flexible for datasets with varying ranges. 2️⃣ Symmetric Linear Quantization: Simple and hardware-friendly. Types of LLM Quantization 🔸 Post-Training Quantization (PTQ): Quick and efficient, applied after training. 🔸 Quantization-Aware Training (QAT): Yields higher accuracy by training with quantization in mind. Quantization isn't just about making models smaller; it's about making them smarter, scalable, and accessible for everyone. Stay tuned for the next update as we explore advanced techniques for quantization and their real-world applications! #LLM #FineTuning #Quantization #AI #MachineLearning #Optimization

🚀 Excited to announce a major technical achievement for our AI interviews platform. We've integrated advanced computer vision based proctoring using OpenCV, an open source computer vision library. 🎉 With robust algorithms and extensive documentation, I was able to integrate this cutting-edge proctoring tech within a few days of dev work. Can't imagine, 6 months back, we had to partner with a third party service provider to do this. Using OpenCV, we've implemented: - Real-time Facial Recognition: Via Haar Cascades and Deep Neural Networks (DNNs) for candidate verification. - Behavioral Analysis: Utilizing optical flow and pose estimation to detect suspicious activities. - Environmental Scanning: Using background subtraction and image segmentation for environment verification. Key Data Points - Adoption: OpenCV is used by 47,000+ companies, proving its reliability. - Accuracy: Achieving 95% accuracy in facial recognition with state-of-the-art DNNs. #GenerativeAI #ComputerVision #OpenSource #AIInterviews #Proctoring #OpenCV #MachineLearning

The trilemma of AI/LLMs. Legal (privacy/confidentiality/copyright), accuracy and environmental (resource) issues. ... For getting more accurate results, we need more training data, which could raise more legal issues. ... Also, to get more accurate results from the huge training data, we need more computational power and thus more resources, which raises environmental issues. It reminds me how at Translating Europe Forum #TEF2024 ... we talked much about the accuracy part (how to use AI in translation and terminology management to get the best results), ... the legal issues arose a bit here and there (like is everything available on the Internet also not copyright-protected and thus ok to feed to AI), ... and the environmental issues were several times raised in the questions from the audience and chat, but not really addressed by any speakers nor panelists. Is this something that will continue to evolve - from accuracy issues to legal and then environmental? ... Has it been happening in some other areas - the first concern being about whether it works right and gives the expected results, then whether this working is legal, and after that whether it is also environmentally reasonable? ... What if we included all these different concerns right from the beginning?

Ever want to learn about the technology behind Diffusion models like mid journey and stable diffusion? Want to build your own diffusion model in PyTorch? Join me at #gtc24 where I will be instructing the NVIDIA DLI Course - Generative AI with Diffusion Models. You’ll learn how to: Build a U-Net to generate images from pure noise. Improve the quality of generated images with the denoising diffusion process. Compare Denoising Diffusion Probabilistic Models (DDPMs) with Denoising Diffusion Implicit Models (DDIMs). Control the image output with context embeddings. Generate images from english text-prompts using CLIP. I will be supported by a team of teaching assistants from NVIDIA and our Partners during this hands on workshop. Dora Csillag|Kim Ng|Ana Caklovic|Mahsa Lotfollahi|Michael O'Keeffe|Janaki Vamaraju, Ph.D.|Paul Gibbons|Ekaterina Sirazitdinova|Mireille F.|Xuemeng (Maggie) Zhang|Pedro Mário Silva You can find more information at: https://lnkd.in/gVS7nHz8 #deeplearning #NVDLI #generativeai #diffusionmodels