Neural Networks and Deep Learning

In the first course of the Deep Learning Specialization, you will study the foundational concept of neural networks and deep learning. By the end, you will be familiar with the significant technological trends driving the rise of deep learning; build, train, and apply fully connected deep neural networks; implement efficient (vectorized) neural networks; identify key parameters in a neural network’s architecture; and apply deep learning to your own applications. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI.

• Introduction to Deep Learning
• Analyze the major trends driving the rise of deep learning, and give examples of where and how it is applied today.
• Neural Networks Basics
• Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
• Shallow Neural Networks
• Build a neural network with one hidden layer, using forward propagation and backpropagation.
• Deep Neural Networks
• Analyze the key computations underlying deep learning, then use them to build and train deep neural networks for computer vision tasks.