Apache MXNet is the deep learning framework which has its origins at Amazon Web Services (AWS) and is a powerful alternative to TensorFlow. This course teaches you how to build dynamic and static computation graphs using the Gluon API.
Apache MXNet offers low-level and high-level APIs which is key to efficiently build neural networks. It also allows you to construct static and dynamic graphs in a symbolic manner using the Module API, the Symbol API, or the Gluon API. In this course, Building Deep Learning Models Using Apache MXNet, you'll learn the basic building blocks of building neural networks using NDArrays, the Module API, the Symbol API, as well as the cutting edge Gluon API. First, you'll gain an understanding of the basic architecture of MXNet and how the basic data structure NDArrays work. Next, you'll discover the difference between symbolic and imperative programming and when you would choose to use one over the other. Then, you'll discover the use of optimizers, loss functions, and data iterators in building and executing neural networks. Finally, you'll explore the Gluon API and build a convolutional neural network for image classification and hybridize it in order to execute a static computation graph. By the end of this course, you'll have the confidence to efficiently build and execute neural networks using all of the APIs that Apache MXNet has to offer.
Apache MXNet offers low-level and high-level APIs which is key to efficiently build neural networks. It also allows you to construct static and dynamic graphs in a symbolic manner using the Module API, the Symbol API, or the Gluon API. In this course, Building Deep Learning Models Using Apache MXNet, you'll learn the basic building blocks of building neural networks using NDArrays, the Module API, the Symbol API, as well as the cutting edge Gluon API. First, you'll gain an understanding of the basic architecture of MXNet and how the basic data structure NDArrays work. Next, you'll discover the difference between symbolic and imperative programming and when you would choose to use one over the other. Then, you'll discover the use of optimizers, loss functions, and data iterators in building and executing neural networks. Finally, you'll explore the Gluon API and build a convolutional neural network for image classification and hybridize it in order to execute a static computation graph. By the end of this course, you'll have the confidence to efficiently build and execute neural networks using all of the APIs that Apache MXNet has to offer.
