Advanced Deep Learning with PyTorch Course

For professionals looking to gain experience in advanced neural network design and optimisation, Oxford Training Center’s Advanced Deep Learning with PyTorch course offers a concentrated, one-week training program. The PyTorch framework and its uses in developing, honing, and implementing deep learning models for a range of industries are thoroughly covered in this course.

Through the use of cutting-edge deep learning architectures, neural network optimisation, and machine learning approaches, participants will obtain hands-on experience. Along with project-based learning and real-world case studies, the program culminates with a Deep Learning Certification to verify the abilities learnt throughout the course.

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Objectives and target group

Objectives

  • This course is designed to give professionals a thorough grasp of PyTorch, including its fundamental features and applications.
  • Proficiency in creating and refining deep learning architectures and neural networks.
  • Hands-on experience using PyTorch methodologies for advanced machine learning and AI model implementation.
  • Understanding of optimisation techniques for neural networks to enhance model performance.
  • Knowledge about applying PyTorch lessons and best practices to implement AI solutions for diverse sectors.

Target Group

This course is ideal for:

  • AI and machine learning professionals eager to enhance their knowledge of advanced tools and techniques.
  • Data scientists seeking to implement scalable and efficient AI models.
  • Researchers and academics interested in exploring the latest innovations in deep learning frameworks.
  • IT professionals and developers aiming to specialize in neural network training and optimization using PyTorch.
  • Anyone passionate about advancing their career in the field of artificial intelligence.

Course Content

1.Introduction to PyTorch Framework

  • Overview of PyTorch and use of PyTorch in deep learning.
  • Key differences of PyTorch from other frameworks.

2.Neural Networks and Deep Learning Architectures

  • Design and train your neural networks from scratch.
  • The study of advanced deep learning architectures: CNN, RNN, Transformer.

3.Advanced PyTorch Techniques

  • Handling complex datasets and custom data loader design.
  • Neural network optimization techniques.
  • 4.Model Training and Evaluation
  • Training Pipelines and Hyperparameter Tuning
  • Techniques for efficient model validation and testing.
  1. 5. Real-World Implementation of AI Model
  • Case studies and industry-based projects.
  • Scalable AI with the deployment of trained models.
  1. 6. Advanced PyTorch Features
  • Working with PyTorch’s dynamic computation graph.
  • Leveraging PyTorch libraries for specialized tasks like image processing and NLP.
  1. 7. Certification and Beyond
  • Completion of a capstone project to demonstrate your knowledge.
  • Deep Learning Certification to showcase professional development.

Course Dates

January 20, 2025
February 10, 2025
March 10, 2025
April 28, 2025

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