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- Getting Started with NVIDIA GPUs : A Beginner...
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275986037
Getting Started with NVIDIA GPUs : A Beginners Guide to AI Acceleration with python, C++ and Cuda
Item #:
275986037
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- Unlock the Real Power of Your NVIDIA GPU — Build, Accelerate, and Deploy Modern AI SystemsModern AI runs on one engine: the GPU. Whether you're training neural networks, running LLMs, building computer-vision models, or fine-tuning transformers, this beginner-friendly guide shows you exactly how to turn your NVIDIA GPU into a high-performance AI workstation.This book cuts through confusing documentation, version conflicts, and setup failures, and shows you step-by-step how to set up drivers, CUDA, cuDNN, environments, and deep learning frameworks the right way.What This Book Allows You to DoInstall and configure NVIDIA GPUs with confidenceUnderstand CUDA Cores, Tensor Cores, RT Cores, and how they impact AITrain deep learning and computer vision models on your own machineRun LLMs locally using PyTorch, Transformers, and quantizationOptimize models for maximum speed using TensorRT, ONNX, and mixed precisionDeploy AI workloads from desktop to edge devices like JetsonAbout the TechnologyNVIDIA GPUs are the backbone of modern AI, supporting advanced parallel computation, matrix math acceleration, and massive throughput required for deep learning. Technologies like CUDA, cuDNN, TensorRT, and NGC containers form the essential toolkit for building high-performance AI systems. This book demystifies them with hands-on labs, simple explanations, and real engineering workflows.Book SummaryThis guide begins at the silicon level, explaining CUDA cores, memory bandwidth, Tensor Cores, and architectural generations from Pascal to Blackwell, so you truly understand the hardware powering your AI stack. From there, you learn how to install drivers, configure CUDA, set up cuDNN, and build clean environments in Python, Docker, and NGC.The second half of the book turns theory into action. You'll train models, accelerate them, optimize for speed, deploy object detection systems, fine-tune LLMs, use mixed precision, and operate real-time inference pipelines. Whether you're a beginner or a self-taught developer, this book takes you from setup to full AI engineering capability using tools used by real-world professionals.What’s Inside This Book?Clear hardware fundamentals: CUDA Cores, Tensor Cores, VRAM, bandwidth, architecturesDriver & CUDA setup without the headaches: avoid the Linux/Nouveau pitfallsHands-on CUDA C++ examples: write real GPU kernels step-by-stepDeep Learning with PyTorch: tensors, training loops, mixed precision, dataloadersComputer Vision workflows: ResNet, EfficientNet, YOLO, augmentation, fine-tuningLLM & NLP engineering: Transformers, quantization (4-bit, 8-bit), LoRAGenerative AI workflows: diffusion models, xFormers, ComfyUI, A1111Model optimization & deployment: TensorRT, ONNX Runtime, Triton ServerEdge AI engineering: Jetson devices, power constraints, deployment pipelinesAbout the ReaderThis book is written for beginners, self-learners, students, and developers who want to understand and fully utilize their NVIDIA GPU for AI. No advanced math or prior deep learning knowledge is required, only basic command-line familiarity and the desire to build real AI systems on your own machine.If you're ready to unlock the true power of your NVIDIA GPU and build AI systems with confidence, scroll up and get your copy today. Your journey into accelerated computing starts now.
| Publication date | December 3, 2025 |
| Language | English |
| File size | 732 KB |
| Screen Reader | Supported |
| Enhanced typesetting | Enabled |
| X-Ray | Not Enabled |
| Word Wise | Not Enabled |
| Print length | 249 pages |
| Page Flip | Enabled |
| Item Weight | 1.5 lbs (680 grams) |
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Getting Started with NVIDIA GPUs : A Beginners Guide to AI Acceleration with python, C++ and Cuda
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DKK 129
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Features & Benefits
- Transform your NVIDIA GPU into a high-performance AI workstation.
- Step-by-step guidance on installing drivers, CUDA, and deep learning frameworks.
- Hands-on labs covering CUDA cores, Tensor Cores and memory bandwidth.
- Learn to train, optimize, and deploy deep learning models effectively.
- Use practical tools like TensorRT and ONNX for maximum speed.
- Designed for beginners to develop real AI engineering skills.
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