Fix NVIDIA Typo (#16728)

docs/en/integrations/kaggle.md

@@ -48,7 +48,7 @@ These options include:
 
 When working with Kaggle, you might come across some common issues. Here are some points to help you navigate the platform smoothly:
 
-- **Access to GPUs**: In your Kaggle notebooks, you can activate a GPU at any time, with usage allowed for up to 30 hours per week. Kaggle provides the Nvidia Tesla P100 GPU with 16GB of memory and also offers the option of using a Nvidia GPU T4 x2. Powerful hardware accelerates your machine-learning tasks, making model training and inference much faster.
+- **Access to GPUs**: In your Kaggle notebooks, you can activate a GPU at any time, with usage allowed for up to 30 hours per week. Kaggle provides the NVIDIA Tesla P100 GPU with 16GB of memory and also offers the option of using a NVIDIA GPU T4 x2. Powerful hardware accelerates your machine-learning tasks, making model training and inference much faster.
 - **Kaggle Kernels**: Kaggle Kernels are free Jupyter notebook servers that can integrate GPUs, allowing you to perform machine learning operations on cloud computers. You don't have to rely on your own computer's CPU, avoiding overload and freeing up your local resources.
 - **Kaggle Datasets**: Kaggle datasets are free to download. However, it's important to check the license for each dataset to understand any usage restrictions. Some datasets may have limitations on academic publications or commercial use. You can download datasets directly to your Kaggle notebook or anywhere else via the Kaggle API.
 - **Saving and Committing Notebooks**: To save and commit a notebook on Kaggle, click "Save Version." This saves the current state of your notebook. Once the background kernel finishes generating the output files, you can access them from the Output tab on the main notebook page.
@@ -101,7 +101,7 @@ Training a YOLO11 model on Kaggle is straightforward. First, access the [Kaggle
 
 Kaggle offers several advantages for training YOLO11 models:
 
-- **Free GPU Access**: Utilize powerful GPUs like Nvidia Tesla P100 or T4 x2 for up to 30 hours per week.
+- **Free GPU Access**: Utilize powerful GPUs like NVIDIA Tesla P100 or T4 x2 for up to 30 hours per week.
 - **Pre-installed Libraries**: Libraries like TensorFlow and PyTorch are pre-installed, simplifying the setup.
 - **Community Collaboration**: Engage with a vast community of data scientists and machine learning enthusiasts.
 - **Version Control**: Easily manage different versions of your notebooks and revert to previous versions if needed.

docs/en/integrations/openvino.md

@@ -148,7 +148,7 @@ This table represents the benchmark results for five different models (YOLOv8n,
 
 ### Intel Arc GPU
 
-Intel® Arc™ represents Intel's foray into the dedicated GPU market. The Arc™ series, designed to compete with leading GPU manufacturers like AMD and Nvidia, caters to both the laptop and desktop markets. The series includes mobile versions for compact devices like laptops, and larger, more powerful versions for desktop computers.
+Intel® Arc™ represents Intel's foray into the dedicated GPU market. The Arc™ series, designed to compete with leading GPU manufacturers like AMD and NVIDIA, caters to both the laptop and desktop markets. The series includes mobile versions for compact devices like laptops, and larger, more powerful versions for desktop computers.
 
 The Arc™ series is divided into three categories: Arc™ 3, Arc™ 5, and Arc™ 7, with each number indicating the performance level. Each category includes several models, and the 'M' in the GPU model name signifies a mobile, integrated variant.
 

docs/en/yolov5/environments/docker_image_quickstart_tutorial.md

@@ -12,7 +12,7 @@ You can also explore other quickstart options for YOLOv5, such as our [Colab Not
 
 ## Prerequisites
 
-1. **NVIDIA Driver**: Version 455.23 or higher. Download from [Nvidia's website](https://www.nvidia.com/Download/index.aspx).
+1. **NVIDIA Driver**: Version 455.23 or higher. Download from [NVIDIA's website](https://www.nvidia.com/Download/index.aspx).
 2. **NVIDIA-Docker**: Allows Docker to interact with your local GPU. Installation instructions are available on the [NVIDIA-Docker GitHub repository](https://github.com/NVIDIA/nvidia-docker).
 3. **Docker Engine - CE**: Version 19.03 or higher. Download and installation instructions can be found on the [Docker website](https://docs.docker.com/get-started/get-docker/).