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Deep Learning in Industrial Applications Deep learning (DL) technologies are proliferating in many areas. DL will transform the entire landscape of Internet-of-Things, from the datacenter architecture, software stack, development processes, to business models. Preferred Networks, Inc. (PFN) was founded in 2014 with the objective of applying this emerging technology to applications of significant industrial importance. This whitepaper discusses the implications of DL in the industrial sector and demonstrates the strong expertise and capabilities of PFN in this area. Quantum-Leap in Analytics DL is expected to revolutionize data analytics that are currently based on traditional statistical modeling or conventional machine learning techniques with two distinctive features. Firstly, DL models can easily handle extremely high-dimensional data. In traditional statistical modeling, the number of independent variables (input variables) is relatively small, which forces data scientists to disregard many potentially significant but seemingly irrelevant input variables. One important example of high-dimensional data is time-series data, which is often prevalent in sensor data from industrial devices. DL can capture the interactions between thousands or even millions of input variables, and make every piece of information from complex interactions that contribute to the output, resulting in significantly higher accuracy than conventional methods. Furthermore, DL is also model-free, meaning that it does not assume a priori knowledge on the class of probability distribution, as any probability distribution can be approximated by a sufficiently complex neural network. This frees data scientists from making too many assumptions (which might also be incorrect or over-simplistic) in advance and from exploring the enormous space of possible statistical models. These two characteristics enable DL to be applied to a very wide range of application areas and to scale to large volumes of data. Although there are a number of research projects actively being pursued around the world, as of today there are relatively few that apply DL to real-world applications. In particular, we are interested in the industrial sector such as automotive and industrial robotics, where an enormous amount of sensor data is generated but only traditional statistical modeling is commonly used. In the course of collaborating with industry leaders like Toyota and Fanuc, we have become convinced that DL technologies can truly revolutionize data analytics in this domain, have built up experiences and knowledge of how DL can be applied in a variety of settings, and come up with a number of innovative ideas. We briefly review them in the following three areas of recognition, prediction, and control.

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産業応用における深層学習深層学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、Internet-of-Thingsの全体像を変革します。 Preferred Networks、Inc.（PFN）は、このeを適用する目的で2014年に設立されました

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Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL transforms the overall picture of Internet-of-Things, including data center architecture, software stack, development process, business model. Preferred Networks, Inc. (PFN) applies this e

INTO JAPANESE

産業応用における深い学習深い学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、インターネットオブフィールスの全体像を変革します。 Preferred Networks、Inc.（PFN）はこのeを適用します

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Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL transforms the overall picture of the Internet of the Internet, including data center architecture, software stack, development process, business model. Preferred Networks, Inc. (P

INTO JAPANESE

産業応用における深い学習深い学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、インターネットのインターネット全体像を変換します。 Preferred Networks、Inc.（P。

BACK INTO ENGLISH

Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL transforms the Internet's overall image of the Internet, including data center architecture, software stack, development process, business model. Preferred Networks, Inc. (P

INTO JAPANESE

産業応用における深い学習深い学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、インターネットのインターネット全体のイメージを変換します。 Preferred Networks、Inc.（P。

BACK INTO ENGLISH

Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL converts the Internet's entire Internet image such as data center architecture, software stack, development process, business model etc. Preferred Network

INTO JAPANESE

産業応用における深い学習深い学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、インターネットのインターネットイメージ全体を変換します。優先ネットワーク

BACK INTO ENGLISH

Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL converts the entire internet image of the Internet, including data center architecture, software stack, development process, business model. Preferred network

INTO JAPANESE

産業応用における深い学習深い学習（DL）技術は多くの分野で普及しています。 DLは、データセンターアーキテクチャ、ソフトウェアスタック、開発プロセス、ビジネスモデルなど、インターネットのインターネットイメージ全体を変換します。優先ネットワーク

BACK INTO ENGLISH

Deep learning in industrial applications Deep learning (DL) technology is spreading in many fields. DL converts the entire internet image of the Internet, including data center architecture, software stack, development process, business model. Preferred network