The document discusses recent developments in artificial intelligence and how AI is being applied in various fields such as deep learning, the internet of things, and virtual/augmented/mixed reality technologies. It provides examples of companies working on AI and highlights some of the challenges and opportunities that AI presents going forward, including the need for further research and development efforts to fully realize the potential of AI.
Despite the existence of data analysis tools such as R, SQL, Excel and others, it is still insufficient to cope with today's big data analysis needs.
The author proposes a CUI (Character User Interface) toolset with dozens of functions to neatly handle tabular data in TSV (Tab Separated Values) files.
It implements many basic and useful functions that have not been implemented in existing software with each function borrowing the ideas of Unix philosophy and covering the most frequent pre-analysis tasks during the initial exploratory stage of data analysis projects.
Also, it greatly speeds up basic analysis tasks, such as drawing cross tables, Venn diagrams, etc., while existing software inevitably requires rather complicated programming and debugging processes for even these basic tasks.
Here, tabular data mainly means TSV (Tab-Separated Values) files as well as other CSV (Comma Separated Value)-type files which are all widely used for storing data and suitable for data analysis.
The document discusses recent developments in artificial intelligence and how AI is being applied in various fields such as deep learning, the internet of things, and virtual/augmented/mixed reality technologies. It provides examples of companies working on AI and highlights some of the challenges and opportunities that AI presents going forward, including the need for further research and development efforts to fully realize the potential of AI.
Despite the existence of data analysis tools such as R, SQL, Excel and others, it is still insufficient to cope with today's big data analysis needs.
The author proposes a CUI (Character User Interface) toolset with dozens of functions to neatly handle tabular data in TSV (Tab Separated Values) files.
It implements many basic and useful functions that have not been implemented in existing software with each function borrowing the ideas of Unix philosophy and covering the most frequent pre-analysis tasks during the initial exploratory stage of data analysis projects.
Also, it greatly speeds up basic analysis tasks, such as drawing cross tables, Venn diagrams, etc., while existing software inevitably requires rather complicated programming and debugging processes for even these basic tasks.
Here, tabular data mainly means TSV (Tab-Separated Values) files as well as other CSV (Comma Separated Value)-type files which are all widely used for storing data and suitable for data analysis.
This slides were used at "5th Machine Learning 15minetes!" http://machine-learning15minutes.connpass.com/event/40294
Introduce important things to tackle machine learning in a company.
Invitation to development tools オープン系開発ツールへのいざないSatoru Yoshida
Git, Docker, Kubernetes, Jenkins などのキーワードを耳にされているかとおもいます。しかしながら、取り組みを始めようとしても、心理的なハードルがあるかもしれません。
このセッションではプログラム開発現場で使用されることの多いツール群、およびイグアスでお取扱いのある製品を紹介します。 You may have heard keywords such as Git, Docker, Kubernetes, Jenkins.
However, there may be psychological hurdles when trying to get started. In this session, we will introduce the tools that are often used in the program development field and the products that Iguazu handles.
14. 2.5x Faster CPU-GPU Data
Communication via NVLink
NVLink
80 GB/s
GPU
P8
GPU GPU
P8
GPU
PCIe
32 GB/s
GPU
x86
GPU GPU
x86
GPU
No NVLink between CPU &
GPU for x86 Servers: PCIe
Bottleneck
NVIDIA P100 Pascal GPU
“Minsky”
POWER8 NVLink Server
x86 Servers with PCIe
• GPUアクセラレーター使用用途にカスタ
マイズされた高速サーバー
• 他社比 2.5倍速のCPU-GPU間接続で、高
速GPUに常にデータを供給
15. 0
20
40
60
80
100
120
140
x86 with 4x M40 / PCIe Power8 with 4x P100 /
NVLink
Training time (minutes):
AlexNet and Caffe to top-1,
50% Accuracy
(Lower is better)
0:00
1:12
2:24
3:36
4:48
6:00
7:12
8:24
x86 with 8x M40 / PCIe Power8 with 4x P100 /
NVLink
BVLC Caffe vs IBM Caffe /
VGGNet
Time toTop-1 50% accuracy:
(Lower is better)
15
S822LC/HPC with 4 Tesla P100
Tesla GPUs is 24% Faster than
8x Tesla M40 GPUs
S822LC/HPC with 4 Tesla
P100 GPUs is 2.2x Faster
than 4x Tesla M40 GPUs