PyTorch Basics

PyTorch – Use in AI is an intensive course tailored for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals interested in mastering this powerful deep learning framework. The course covers PyTorch fundamentals, neural network construction, model training, and real-world applications, preparing participants to tackle complex AI challenges in various industries.

• Mastering PyTorch: Gain comprehensive knowledge and hands-on experience with PyTorch.
• Neural Network Proficiency: Become proficient in designing and implementing various types of neural networks.
• Practical AI Solutions: Develop practical AI solutions that can be deployed in real-world environments.

1. Introduction to PyTorch:
   • Basics of PyTorch, its comparison with other AI frameworks, and initial setup instructions.
2. Building Blocks of Neural Networks:
   • Detailed exploration of layers, activation functions, and the construction and debugging of neural networks.
3. Training Models:
   • In-depth coverage of loss functions, optimization algorithms, and effective training and validation techniques.
4. Advanced PyTorch:
   • Advanced topics such as convolutional and recurrent neural networks, transfer learning, and fine-tuning.
5. Real-World Applications:
   • PyTorch applications in industries like healthcare and autonomous vehicles, and strategies for deploying PyTorch models.
6. Projects and Assessments:
   • A capstone project that encompasses designing, building, and presenting a PyTorch-based AI solution.