Complete AI Dev Environment: Tensorflow and PyTorch with Nvidia Cuda in Windows 11
Welcome to our comprehensive guide on setting up the ultimate AI development environment on Windows 11! Whether you're a beginner or a seasoned developer, this step-by-step tutorial will help you get TensorFlow and PyTorch running seamlessly with Nvidia CUDA.
🔧 What You'll Learn:
Installation of TensorFlow and PyTorch: Detailed walkthrough on installing these powerful deep learning libraries.
Setting Up Nvidia CUDA: Ensure your system is optimized for high-performance computing.
Configuration Tips: Best practices for configuring your environment for maximum efficiency.
💡 Why This Setup?
Boost Your AI Projects: Leverage the power of GPU acceleration to enhance your AI and machine learning models.
Windows 11 Compatibility: Learn how to navigate the new features and settings in Windows 11 for AI development.
Hands-On Experience: Practical steps and real
-world examples to help you understand the process.
🖥️ Key Sections:
0:00 Introduction
0:42 Installing TensorFlow
5:05 Installing PyTorch
6:20 Setting Up Jupyter Lab and Ipykernel
07:48 Troubleshooting Jupyter lab
08:36 Test GPU with Jupyter lab
09:10 Conclusion
📌 Tags:
#AI #MachineLearning #DeepLearning #TensorFlow #PyTorch #NvidiaCUDA #Windows11 #AIDevelopment #TechTutorial #Programming #Coding #ArtificialIntelligence #DataScience
Пікірлер: 2
Great video! The step-by-step walkthrough for installing TensorFlow and PyTorch with NVIDIA CUDA on Windows 11 is incredibly helpful. As someone just getting started in AI/ML, content like this is invaluable. Tutorials that demystify the setup process and provide clear, actionable instructions make a big difference. I'm sure a lot of your viewers, myself included, really appreciate the time and effort you've put into creating these types of informative, hands-on guides. Please keep making more videos like this! They are incredibly motivating for those of us trying to build our AI development skills. The more accessible you can make the technical setup, the more people will be empowered to dive into practical machine learning projects. Looking forward to seeing what other helpful tutorials you create in the future.
but you have not mention ml folder in ml folder . in previous video will it be any problem if i activate the env for (Preview (Nightly) )that we previously created ,and do the stuff of this video like tensorflow, Stable (2.3.1)