Training > AI & ML > PyTorch Associate Training
INSTRUCTOR-LED COURSE

PyTorch Associate Training

Whether you aim to launch your deep learning journey, strengthen your current ML/DL capabilities, or gain an industry-recognized credential, this training offers a structured yet hands-on learning experience. Through a blend of lectures, demonstrations, guided labs, and assessments, students will not only understand PyTorch’s ecosystem but will also build and optimize real-world models from scratch.

Who Is It For

This course is for aspiring data scientists and ML engineers, software developers entering the ML/DL space, researchers and analysts incorporating AI into their work, and professionals seeking PyTorch certification.
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What You’ll Learn

Upon completion, participants will be able to build, optimize, and deploy deep learning models with PyTorch.
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What It Prepares You For

Graduates who have experience applying the skills learned in this course will be well-prepared to sit for the PyTorch Foundation PyTorch Certified Associate exam, strengthening both their skillset and professional credentials.
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Course Outline
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PyTorch Fundamentals
- Understanding Tensors
- Tensor Operations
- Computation Graphs
Hardware Acceleration
-Leveraging CPU, CUDA, and MPS for high-performance computing
Data Handling
- Loading
- Transforming
- Batching Datasets using Dataset and DataLoader
Model Development
- Designing
- Training
- Validating
- Deploying Neural Networks
Core Modules
- Mastering autograd
- Optimizers
- Loss Functions
Advanced Features
- Implementing torch.compile
- Managing Distributed Model Training
Performance Optimization
- Using Automatic Mixed Precision (AMP)
- PyTorch Profiler
Specialized Applications
- NLP
- Computer Vision
- Speech Recognition
Certification Readiness
- Key Concepts
- Practice
- Exercises

Prerequisites
Students should have proficiency in Python programming, familiarity with Jupyter Notebooks, and a basic understanding of machine learning concepts and Google Colab.