In this talk I will discuss recent trends and developments in deep learning research. I'll then touch on the significant progress that this research has produced in a number of areas, including computer vision, language understanding, translation, healthcare, and robotics. These advances are driven by both new algorithmic approaches to some of these problems, and by the ability to scale computation for training ever large models on larger datasets. I'll discuss both of these issues. Finally, one of the reasons for the rapid spread of the ideas and techniques of deep learning has been the availability of open source libraries such as TensorFlow. I'll give an overview of why these software libraries have an important role in making the benefits of machine learning available throughout the world.