Implementing and training deep learning models in a free, integrated environment
This course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI.
What you’ll learn
- This course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI..
- Learn how to utilize Google Colab as an online computing platform in deep learning projects, including running Python code, using a free GPU, and working with external files and folders.
- Understand the general workflow of a deep learning project.
- Examine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learning.
- Learn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they address.
- Gain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices.
Course Content
- Getting started in Google Colab –> 10 lectures • 21min.
- The ecosystem of Google Colab –> 6 lectures • 30min.
- Introduction to PyTorch –> 12 lectures • 1hr 14min.
- Working with datasets –> 6 lectures • 43min.
- Recognizing handwritten digits –> 9 lectures • 1hr.
- Transfer learning for object recognition –> 6 lectures • 41min.
- Recognizing fashion items –> 7 lectures • 43min.
- Deep learning best practices –> 5 lectures • 30min.
Requirements
This course covers the general workflow of a deep learning project, implemented using PyTorch in Google Colab. At the end of the course, students will be proficient at using Google Colab as well as PyTorch in their own projects. Students will also learn about the theoretical foundations for various deep learning models and techniques, as well as how to implement them using PyTorch. Finally, the course ends by offering an overview on general deep learning and how to think about problems in the field; students will gain a high-level understanding of the role deep learning plays in the field of AI.
- Learn how to utilize Google Colab as an online computing platform in deep learning projects, including running Python code, using a free GPU, and working with external files and folders
- Understand the general workflow of a deep learning project
- Examine the various APIs (datasets, modeling, training) PyTorch offers to facilitate deep learning
- Learn about the theoretical basis for various deep learning models such as convolutional networks or residual networks and what problems they address
- Gain an overview understanding of deep learning in the context of the artificial intelligence field and its best practices