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