Building Deep Learning Models Using Apache MXNet

Apache MXNet provides both low-level and higher-level APIs, which are crucial for efficiently building neural networks. You can also create dynamic and static graphs in a symbolic fashion using the Module API or the Symbol API. This course, Building Deep Learning models using Apache MXNet, will teach you the fundamental building blocks of building neural network using NDArrays. It also includes the Module API, Symbol API, and the cutting-edge Gluon API. You'll first learn about MXNet's basic architecture and the data structure NDArrays. Then, you will learn the differences between imperative and symbolic programming and when to choose one. Next, you will learn about optimizers, loss function, and data iterators when building and executing neural network. The Gluon API will be explored and you'll create a convolutional neural system for image classification. You can then hybridize it to run a static computation graph. This course will equip you with the skills and confidence to create and execute neural networks efficiently using all the APIs available to Apache MXNet.