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.
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.
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.
- 5. Real-World Implementation of AI Model
- Case studies and industry-based projects.
- Scalable AI with the deployment of trained models.
- 6. Advanced PyTorch Features
- Working with PyTorch’s dynamic computation graph.
- Leveraging PyTorch libraries for specialized tasks like image processing and NLP.
- 7. Certification and Beyond
- Completion of a capstone project to demonstrate your knowledge.
- Deep Learning Certification to showcase professional development.