Mentor Based
PyTorch Basics
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Course Overview
Program Syllabus
Module 1
About This Course
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.
Module 2
Aim
This course aims to provide an in-depth understanding of PyTorch, its applications in AI from basic concepts to advanced model architectures, and hands-on guidance on building and deploying neural network models.
Module 3
Program Objectives
- 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.
Module 4
Program Structure
- Introduction to PyTorch:
- Basics of PyTorch, its comparison with other AI frameworks, and initial setup instructions.
- Building Blocks of Neural Networks:
- Detailed exploration of layers, activation functions, and the construction and debugging of neural networks.
- Training Models:
- In-depth coverage of loss functions, optimization algorithms, and effective training and validation techniques.
- Advanced PyTorch:
- Advanced topics such as convolutional and recurrent neural networks, transfer learning, and fine-tuning.
- Real-World Applications:
- PyTorch applications in industries like healthcare and autonomous vehicles, and strategies for deploying PyTorch models.
- Projects and Assessments:
- A capstone project that encompasses designing, building, and presenting a PyTorch-based AI solution.
Module 5
Who Should Enrol?
- Advanced students and professionals in M.Tech, M.Sc, and MCA programs.
- E0 & E1 level professionals in fields such as IT, BFSI, consulting, and fintech.
Module 6
Program Outcomes
- Advanced PyTorch Skills: Proficiency in using PyTorch for AI model development and deployment.
- Industry Readiness: Preparedness to apply AI skills in real-world industry settings.
- Innovative Thinking: Enhanced ability to innovate in AI with the latest PyTorch techniques.
Module 8
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- 1:1 project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
Module 9
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 10
Publication Opportunity
Get published in a prestigious open-access journal.
Module 11
Centre of Excellence
Become part of an elite research community.
Module 12
Networking & Learning
Connect with global researchers and mentors.
Module 13
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 14
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Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
Limited SeatsClosing Soon
PyTorch Basics
Professional Certification Program
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FormatLive + Recorded
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Duration8 Weeks
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CertificationVerified
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