Deep learning is a subfield of machine learning and is known for powering AI applications and services that perform analytical and physical tasks without human intervention. Example use cases for deep learning include chatbots, medical image recognition technologies and fraud detection. However, as with machine learning, designing and running a deep learning algorithm requires a tremendous amount of human effort as well as compute power.
The IBM Research team has explored one of the most complex and time-consuming processes in deep learning: the creation of the neural architecture through a technique called neural architecture search (NAS). The team reviewed the NAS methods developed and presented the benefits of each with a goal of helping practitioners choose an appropriate method. Automating the approach to finding the best-performing architecture for a machine learning model can lead to greater democratization of AI, but the issue is complex and difficult to solve.
With the Deep Learning service within IBM Watson Studio, you can still get started with deep learning quickly. The service helps you design complex neural networks and then experiment at scale to deploy an optimized machine learning model. Designed to simplify the process of training models, the service also provides an on-demand GPU compute cluster to address compute power requirements. You can also integrate popular open source ML frameworks such as TensorFlow, Caffe, Torch and Chainer to train models on multiple GPUs and accelerate results. On IBM Watson Studio, you can combine AutoML, IBM AutoAI, and the Deep Learning service to accelerate experimentation, analyze structured and unstructured data, and deploy better models faster. \r