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Udemy - Build On-Device Ai - OneDDL - 02-26-2025 Free Download Udemy - Build On-Device Ai Published: 2/2025 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 664.48 MB | Duration: 1h 54m Master On-Device AI! Learn to Train, Compile and Profile AI Models for Edge Device deployement with Qualcomm AI Hub What you'll learn Understand the complete workflow of On-Device AI deployment, from training to inference Learn how to use Qualcomm AI Hub for managing, compiling, and optimizing AI models Master model profiling and compilation to enhance performance on edge devices Learn quantization techniques to optimize AI models for mobile, IoT, and embedded systems Understand the difference between symmetric and asymmetric quantization Requirements Basic Python knowledge is recommended, but no prior AI experience is required Description If you are a developer, data scientist, or AI enthusiast looking to create deployment-ready efficient AI models for edge devices, this course is for you. Do you want to accelerate AI inference while reducing computational overhead? Are you looking for practical techniques to optimize your models for mobile, IoT, and embedded systems?This course will teach you how to train, compile, profile, and optimize AI models, ensuring they run efficiently on resource-constrained devices without compromising performance.In this course, you will:1. Learn the complete workflow of On-Device AI Deployment - from training to inference.2. Understand Qualcomm AI Hub and how to use it for AI model management.3. Explore model compilation and profiling to enhance performance.4. Implement inference techniques for deploying models on edge devices.5. Master quantization techniques to optimize AI models for low-power hardware.Why Learn On-Device AI?Deploying AI on edge devices allows you to reduce latency, enhance privacy, and optimize performance without depending on cloud computing. By mastering quantization, model profiling, and efficient AI deployment, you can ensure your models run faster, consume less power, and are ready for real-world applications like mobile AI, autonomous systems, and IoT.Throughout the course, you'll gain hands-on experience with real-world AI deployment scenarios. You will balance theory and practical application to make your models leaner, smarter, and deployment-ready.By the end of the course, you'll be equipped with the skills to train, optimize, and deploy AI models on edge devices, making you a valuable asset in the field of AI deployment.Ready to take your AI models to the next level? Enroll now and start your journey! Overview Section 1: Introduction Lecture 1 Introduction Section 2: On-Device Introduction & Setup Lecture 2 On-Device Introduction Lecture 3 Qualcomm AI Hub Introduction Lecture 4 Qualcomm AI Hub Login Section 3: Model Training & Deployment Steps Lecture 5 Steps for On-Device Deployment Lecture 6 Model Training Phase - Theory Lecture 7 Training the Model- Practical Section 4: Model Compilation & Profiling Lecture 8 Compiling the Model - Theory Lecture 9 Compiling the Model - Practical Lecture 10 Profiling the Model - Theory Lecture 11 Profiling the Model - Practical Section 5: Model Inference & Deployment Lecture 12 Inference Lecture 13 Downloading the Model Section 6: Model Optimization & Quantization Lecture 14 Introduction to Quantization Lecture 15 Symmetrics quantization Lecture 16 Asymmetrics Quantization Lecture 17 Quantization Techniques - Practical Application Section 7: Conclusion Lecture 18 About your certificate Lecture 19 Bonus lecture Beginners in machine learning looking to gain hands-on experience in model optimization and on-device AI deployment,AI professionals, data scientists, and students who want to optimize models for deployment on resource-constrained devices like mobile, IoT, and embedded systems,Developers and engineers interested in learning how to use Qualcomm AI Hub to compile, profile, and deploy efficient AI models Homepage: [To see links please register or login] Recommend Download Link Hight Speed | Please Say Thanks Keep Topic Live [To see links please register or login] No Password - Links are Interchangeable