Comprehensive course analysis
About the 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
Dave DeBarr has more than 18 years of experience using machine learning to implement high-performance solutions for a variety of commercial and government applications. DeBarr is a principal applied researcher at Microsoft, currently focusing on authentication and account security. In his time at...
Aitzaz Ahmad is an applied scientist specializing in machine learning at Amazon. He builds high-performance, scalable and reliable machine learning applications for Amazon's finance systems. Previously he was a senior data scientist at Procter & Gamble who led the efforts to deploy machine le...
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