Deep learning has become one of the most important techniques used to address computer vision tasks for autonomous vehicles. Nowadays, using deep learning methodologies, autonomous vehicles are able to gather and process large amounts of data, perform advanced perception, and make complex decisions. This module will provide you a clear understanding of the foundational concepts in deep learning, challenges, applications, and the role that deep learning is playing in the rapid advancement of the field. This module will present, in terms of theory and practice, the fundamental concepts of deep learning, including the foundations of neural network architectures, learning techniques, and architectures.
What You’ll Learn
- Gain a fundamental understanding of deep learning
- Understand the basic theory of neural networks
- Learn different learning techniques
- Identify key architecture parameters
- Build and train deep neural networks
Module Content
Module kick-off
Reading list for background in mathematics and deep learning
You don't currently have access to this content
Introduction to deep learning
Overview, history and challenges for deep learning [11:09]
You don't currently have access to this content
Basic theory
Neural networks [13:59]
You don't currently have access to this content
Activation functions [06:33]
You don't currently have access to this content
Loss functions [08:29]
You don't currently have access to this content
Backpropagation [11:02]
You don't currently have access to this content
Learning techniques
Regularization and normalization [15:56]
You don't currently have access to this content
Supervised learning [09:18]
You don't currently have access to this content
Datasets [09:06]
You don't currently have access to this content
Transfer learning [12:11]
You don't currently have access to this content
Types of neural networks
Self-supervised and unsupervised learning [10:33]
You don't currently have access to this content
Introduction to Convolutional Neural Networks (CNNs) [44:09]
You don't currently have access to this content
Applications of CNNs [16:53]
You don't currently have access to this content
Introduction to Recurrent Neural Networks (RNNs) [29:28]
You don't currently have access to this content
HOMEWORK
Applications of RNNs [07:08]
You don't currently have access to this content
Prep work reading list, to help prepare for below exercises
You don't currently have access to this content
Optional Advanced Reading
Access the 2-part homework tasks
You don't currently have access to this content
End of module reading list, based on more advanced content (optional)
You don't currently have access to this content
Group session recordings
You don't currently have access to this content
Deep Learning and AI end of module test
You don't currently have access to this content