Explore NVIDIA GPU computing and Jetson Nano setup. Learn deep learning with TensorFlow, PyTorch, and CNNs for tasks like traffic sign classification and brain tumor detection. Implement computer vision techniques, YOLO for object detection, and build real-time applications like pedestrian detection and autonomous vehicles.
Learn GPU computing with Jetson Nano, deep learning with TensorFlow and PyTorch, and computer vision techniques. Implement CNNs, YOLO, object detection, and build real-time applications like pedestrian detection and autonomous vehicle
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Introduction to NVIDIA, GPU Computing
Introduction to Jetson Nano
Jetson Nano Basic Setup Tutorial
Introduction to CUDA (Compute Unified Device Architecture), CUDA Memory Hierarchy
Deep Learning Fundamentals, Introduction to Deep Learning
Introduction to Deep Learning Frameworks (TensorFlow, PyTorch) with GPU Support
Building and Training a Simple Neural Network Using Pytorch and TensorFlow
Convolutional Neural Networks (CNNs), CNN Using TensorFlow and PyTorch
Introduction to Computer Vision and Basic Image Processing
Image Smoothing, Edge Detection, and Morphology Techniques
Advanced Image Segmentation and Thresholding Techniques
Image Blending, Pyramids, and Feature Transform Techniques
Sudoku Solver Using OpenCV
Pretrained Models Overview – VGG, ResNet, F-CNN, U-Net
Brain Tumor Classification Using Pre-trained Model
YOLO for Object Detection
Image Segmentation with U-Net
Pytorch Model to TensorRT Conversion
Object Detection with Jetson Nano
Pedestrian Detection with Jetson Nano
Building an Autonomous Vehicle with Deep Learning, Computer Vision, and Jetson Nano
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The Jetson Nano is an affordable single-board computer by NVIDIA designed for AI and machine learning applications. It features a powerful GPU (Graphics Processing Unit) and supports CUDA, allowing for efficient execution of deep learning models, especially in resource-constrained environments like robotics and edge computing.
CUDA (Compute Unified Device Architecture) is NVIDIA’s parallel computing platform and programming model that allows software to utilize the GPU for general-purpose computing tasks. CUDA enables the acceleration of deep learning tasks by parallelizing operations, making it much faster than CPU computation.
TensorFlow is known for its scalability and production-readiness, especially in deploying models to various platforms. PyTorch is more flexible and user-friendly, making it popular for research and prototyping due to its dynamic computational graph. Both frameworks support GPU acceleration.
TensorRT is NVIDIA’s deep learning inference library that optimizes models for faster execution on GPUs. You can convert a PyTorch model to TensorRT using the torch2trt library, which optimizes the model and accelerates inference on Jetson Nano.
YOLO (You Only Look Once) is a deep learning model designed for real-time object detection. It detects multiple objects in images or video by predicting bounding boxes and class probabilities in one forward pass, making it highly efficient for real-time applications.
Brain tumor classification involves detecting and classifying tumors from medical images like MRI scans. Deep learning models, especially CNNs, can automatically extract relevant features and classify images with high accuracy.
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