AutoML and deep learning
AutoML 和深度学习
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
深度学习是机器学习的一个子领域，以支持 AI 应用和服务而出名，这些应用和服务无需人工干预即可执行分析和物理任务。 深度学习的用例包括聊天机器人、医学影像识别技术和欺诈检测。 但是，与机器学习一样，设计和运行深度学习算法需要耗费大量人力和计算能力。 IBM 研究团队探索了深度学习领域最复杂耗时的流程之一： 通过名为神经结构搜索 (NAS) 的技术创建神经结构。 该团队审查了已开发的各种 NAS 方法，展示了各种方法的优势，目的是帮助从业人员选择适当的方法。 借助为机器学习模型寻找最佳性能架构的自动化方法，可以提高 AI 的民主化程度，但这一问题仍十分复杂且难以解决。 利用 IBM Watson Studio 中的深度学习服务，您仍然可以快速着手开展深度学习工作。 该服务可帮助您设计复杂的神经网络，然后大规模开展试验，部署优化的机器学习模型。 该服务旨在简化模型训练流程，同时还按需提供 GPU 计算集群，满足计算能力需求。 您还可以集成热门开源 ML 框架（如 TensorFlow、Caffe、Torch 和 Chainer），以在多个 GPU 上训练模型并加速取得结果。 在 IBM Watson Studio 上，您可以结合使用 AutoML、IBM AutoAI 和深度学习服务，以加快实验速度，分析结构化和非结构化数据，并更快部署更佳模型。