Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

The second course in the Deep Learning Specialization will allow you to explore the deep learning blackbox and understand the processes that are responsible for generating good results.

You will be able to: train and develop test set and analyze bias/variance to build deep learning applications. Learn how to use standard neural networks techniques like initialization, L2 dropout regularization and batch normalization. Implement and apply various optimization algorithms such as Momentum, Momentum and RMSprop. Check for convergence. Finally, implement a TensorFlow neural network. Deep Learning Specialization is our foundational program. It will teach you about the challenges and potential consequences of deep learning, as well as prepare you for participation in the development and deployment of cutting-edge AI technology. This program will help you gain the skills and knowledge to apply machine learning in your work, advance your technical career, or take the ultimate step into the world of AI.