Course Overview

PyTorch – Use in AI is a 12-week intensive course designed for M.Tech, M.Sc, and MCA students, as well as E0 & E1 level professionals who are keen to master PyTorch, a powerful deep learning framework. This course covers PyTorch fundamentals, neural network construction, model training, and real-world applications, equipping participants to solve complex AI challenges in various industries like healthcare and autonomous systems.

Course Goals

The primary goal of this course is to provide participants with an in-depth understanding of PyTorch and its applications in AI, from foundational concepts to advanced model architectures. Through hands-on guidance, participants will learn to build and deploy neural network models effectively.

Program Objectives

• Mastering PyTorch: Gain a comprehensive understanding of PyTorch, including hands-on experience in model building and deployment.
• Neural Network Proficiency: Learn to design, implement, and optimize different types of neural networks using PyTorch.
• Practical AI Solutions: Develop practical AI solutions that can be applied in real-world environments across various industries.

Program Structure

• Module 1: Introduction to PyTorch
  • Overview of PyTorch and how it compares to other AI frameworks
  • Setting up the PyTorch environment and understanding basic operations
• Module 2: Building Blocks of Neural Networks
  • Exploring layers, activation functions, and the construction of neural networks
  • Debugging and troubleshooting network architectures
• Module 3: Training Models
  • Understanding loss functions and optimization algorithms (e.g., Adam, SGD)
  • Best practices for training, validation, and model tuning
• Module 4: Advanced PyTorch
  • Convolutional Neural Networks (CNNs) for image recognition
  • Recurrent Neural Networks (RNNs) for sequence modeling
  • Transfer learning and fine-tuning pre-trained models for custom tasks
• Module 5: Real-World Applications
  • Applying PyTorch in industries like healthcare (medical imaging) and autonomous vehicles
  • Deploying PyTorch models in production environments
• Module 6: Projects and Assessments
  • Capstone project: Design, build, and present a PyTorch-based AI solution that solves a real-world problem
  • Weekly assignments and assessments to reinforce key concepts

Eligibility

• Students: M.Tech, M.Sc, MCA students specializing in AI, machine learning, or data science.
• Professionals: E0 & E1 level professionals in industries like IT, BFSI, consulting, and fintech, seeking to expand their AI expertise.

Learning Outcomes

• Advanced PyTorch Skills: Develop proficiency in using PyTorch for AI model development, from basic neural networks to advanced architectures.
• Industry Readiness: Be prepared to apply AI skills to real-world industry challenges, including healthcare and autonomous systems.
• Innovative Thinking: Cultivate the ability to innovate using the latest deep learning techniques and PyTorch methodologies.