PyTorch Training and Certification in Riyadh

PyTorch Training and Certification in Riyadh, Saudi Arabia

Participants will learn the fundamental concepts of tensors, automatic differentiation, neural network construction, and optimization.

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PyTorch Training and Certification is a professional training program delivered by ProgNXT in Riyadh, Saudi Arabia. ProgNXT, a globally recognized corporate training provider in 55+ countries, offers this PyTorch Training and Certification course with hands-on, instructor-led sessions. This course provides a comprehensive introduction to PyTorch, a powerful deep learning framework widely used for research and production....

Expert Panel

Designed by the ProgNXT AI & Data Science Expert Panel, specializing in Generative AI, Machine Learning, and ChatGPT applications

ProgNXT AI & Data Science Expert Panel

Course Code: TZQ38
14 Hrs
Last Updated: April 2026

Course Rating5/5

This course provides a comprehensive introduction to PyTorch, a powerful deep learning framework widely used for research and production. Participants will learn the fundamental concepts of tensors, automatic differentiation, neural network construction, and optimization. The course covers deep learning model building, training, and deployment, along with hands-on exercises to develop real-world AI applications using PyTorch.

Welcome to the official PyTorch Training and Certification certification program in Riyadh, Saudi Arabia. This comprehensive training is designed to elevate your professional skills and provide you with practical, industry-relevant knowledge locally. As a globally recognized corporate training provider operating in 55+ countries, ProgNXT ensures that our curriculum meets the highest standards of excellence.

Whether you are looking to upskill your team or advance your personal career, our expert-led sessions will guide you through the core concepts of this domain. Upon successful completion of the 14 Hrs program, participants will receive a globally accepted certification, demonstrating their proficiency and readiness to tackle complex challenges in the field.

Basic proficiency in Python programming
Understanding of machine learning and deep learning fundamentals
Familiarity with linear algebra, matrices, and basic neural networks
Experience with NumPy and Pandas is recommended

By the end of the course, participants will be able to:

Understand the core concepts of PyTorch and its architecture
Work with PyTorch tensors and perform operations efficiently
Build and train deep learning models using PyTorch’s neural network modules
Implement backpropagation and optimization techniques
Utilize PyTorch’s DataLoader for efficient dataset management
Deploy PyTorch models for real-world applications
Implement transfer learning and fine-tune pre-trained models

Hands-on experience with PyTorch’s tensor operations and computation graph
Building and training deep learning models using PyTorch
Applying optimization techniques for better model performance
Implementing CNNs, RNNs, and NLP-based models using PyTorch
Deploying trained models for inference in real-world applications
Understanding and leveraging PyTorch Lightning for scalable training

Understanding of PyTorch tensors and operations
Implementing neural networks using PyTorch’s nn.Module
Optimization and loss calculation techniques
Model evaluation, fine-tuning, and deployment strategies
Building a real-world AI application using PyTorch

ProgNXT Certificate of Completion: Participants who complete the training and actively engage in exercises will receive a ProgNXT Certificate of Completion for the Training Program.

Introduction to PyTorch and Tensors

Overview of PyTorch and its ecosystem
Installing and setting up PyTorch
Understanding tensors and tensor operations
Automatic differentiation and computation graphs

Neural Networks and Model Training

Introduction to PyTorch’s torch.nn module
Building feedforward neural networks (FNNs)
Activation functions and loss functions
Optimizers: SGD, Adam, and other gradient-based methods

Advanced PyTorch and Model Deployment

Working with PyTorch’s DataLoader and dataset utilities
Convolutional Neural Networks (CNNs) for image classification
Recurrent Neural Networks (RNNs) for sequence modeling
Transfer learning and fine-tuning pre-trained models
Deploying PyTorch models using TorchScript and ONNX
Hands-on: Implementing and deploying a PyTorch-based AI model

Lecture slides and official PyTorch documentation
Jupyter Notebooks with hands-on exercises
Sample datasets for deep learning training

Participants who complete the training and actively engage in exercises will receive a ProgNXT Certificate of Completion for the Training Program.

PyTorch Training and Certification Events in saudi-arabia

Online Events

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More Upcoming Session Dates

November 2026

📍 Riyadh, Saudi Arabia

5th Nov 2026→6th Nov 2026

🌐 Online Instructor-Led

5th Nov 2026→6th Nov 2026

📍 Riyadh, Saudi Arabia

27th Nov 2026→28th Nov 2026

🌐 Online Instructor-Led

27th Nov 2026→28th Nov 2026

December 2026

📍 Riyadh, Saudi Arabia

2nd Dec 2026→3rd Dec 2026

🌐 Online Instructor-Led

2nd Dec 2026→3rd Dec 2026

🌐 Online Instructor-Led

21st Dec 2026→22nd Dec 2026

📍 Riyadh, Saudi Arabia

28th Dec 2026→29th Dec 2026

Events for other courses in Riyadh, Saudi Arabia

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PyTorch Training and Certification in Riyadh