The PyTorch Fundamentals course by Oxford Training Centre is a comprehensive introduction to PyTorch, one of the fastest-rising popular deep learning frameworks today. This hands-on PyTorch tutorial for beginners will take a week and hence be ideal for those who seek an introduction to PyTorch for AI Development-Deep Learning. Whether you are just starting machine learning, building neural networks, or going into predictive analytics, this course will give full insight into the theory and also practical aspects of these areas.
In this course, the participant will learn the basics of PyTorch in machine learning and hands-on experience in model building using PyTorch. By the end of this course, students will know how PyTorch works for computer vision, data science, and NLP and how to apply those skills to real-world scenarios. The course structure is made in such a way that you don’t just theoretically learn concepts but also have hands-on practice.
Objectives and target group
Objectives
This PyTorch Fundamentals course provides a broad-based foundation for the use of PyTorch in AI, deep learning, and machine learning applications. At the end of this course, you will be able to do the following:
- Have a deep understanding of PyTorch basics, which includes tensor operations and its computation graph. Learn PyTorch from scratch to learn all the basic components one needs to know in order to build machine learning and deep learning models.
- Develop and train neural networks using PyTorch, focusing on how to put the concepts into practice for a variety of application domains: computer vision and NLP.
- Advanced PyTorch: Optimization in PyTorch, model evaluation, and debugging.
- Confidently apply PyTorch to real-world problems in AI, data science, and machine learning..
Target Group
The course is suitable for:
- Beginners in Machine Learning and AI: Those with little to no experience in deep learning or AI can get started with PyTorch with a solid foundation.
- Data Scientists and Analysts: Professionals looking to expand their skills in AI and machine learning with a focus on PyTorch will find this course beneficial.
- Researchers and Developers: Those who want to integrate PyTorch for deep learning into their research or development workflows.
- Students and Enthusiasts: Individuals seeking an introduction to PyTorch for neural networks and practical applications in computer vision and NLP.
Course Content
The PyTorch Fundamentals course covers the essential tools and concepts required to begin working with PyTorch in machine learning and deep learning applications.
Module 1: Introduction to PyTorch
- Overview of PyTorch and its advantages over other frameworks (e.g., PyTorch vs TensorFlow for beginners).
- Basic PyTorch operations: tensors, data types, and GPU acceleration.
- Setting up your PyTorch environment.
Module 2: PyTorch for Neural Networks
- Introduction to neural networks and how PyTorch supports model creation.
- Building basic neural networks with PyTorch.
- Understanding activation functions, loss functions, and optimizers.
Module 3: PyTorch for Deep Learning
- Building more complex deep learning models with PyTorch.
- PyTorch for deep learning: Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs).
- Applying PyTorch for computer vision and natural language processing (NLP).
Module 4: Advanced PyTorch Fundamentals
- Diving into advanced PyTorch fundamentals: transfer learning, model fine-tuning.
- Exploring PyTorch for AI development in real-world applications like predictive analytics.
- Implementing PyTorch for AI and deep learning using real datasets.
Module 5: Practical PyTorch Tutorial
- Hands-on projects where participants apply what they’ve learned to solve real-world problems.
- Creating and training models on custom datasets.
- Understanding PyTorch for data science and how to handle large datasets effectively.