About this Course

Deep learning is a subfield of artificial intelligence that is inspired by how the human brain works, a concept often referred to as neural networks. In the last decade we’ve seen significant development of deep learning methods that enable state-of-the-art performance for many tasks, including image, audio and video classification. In this course, you’ll gain both a theoretical understanding of deep learning and hands-on experience with emerging use cases.

WHAT YOU’LL LEARN

• The underlying conceptual principles of neural networks
• Modern deep learning techniques such as dropout and batch normalization
• How to select appropriate loss functions, optimizers and activation functions
• The application of CNNs, RNNs, VAE and more 
• How to build computer vision models, machine translation system and game playing agents

GET HANDS-ON EXPERIENCE

• Gain practice with cutting-edge techniques, including generative adversarial networks (GANs), reinforcement learning and BERT
• Apply techniques to rapidly build and train deep neural networks using popular open-source tools such as Keras and TensorFlow

EARN A DIGITAL BADGE

After successfully completing this course, you can claim a digital achievement badge. Learn about digital badges.