machine learning anotation

Annotation is critical to AI development and operation.
It is important to create large amount of accurate annotation data
Analysis and improvement of annotation process by machine learning.

Annotations are processes located upstream of pipeline. Therefore, if there are many errors in the annotation, it may have a fatal effect on subsequent processes, including model learning and evaluation(in many cases, evaluation data is also generated by the annotation).

Why is annotation important?
To unify the content to be read from the data.
In the upstream process of the AI pipeline, it has a fatal impact on the leader processes such as model learning and evaluation.




ビッグデータ x Deep learning

>ABEJA Insight for Retail

なるほど、deep learningって、人間の行動の一定パターンなどをデータ化して、傾向を分析したソリューション提示に強いのね。よって、アナリティクスや、公共のサービスなど。。


-Python, Elixir, Go いずれかの言語での開発経験
-クラウド, IaaS(AWS, GCP)での開発経験
-DBやdistributed systems/high availability への理解

-機械学習(特にDeep Learning)に関する実務経験


difference between deep learning and machine learning

先日、deep learningのサービスについて会話をしていたら、私が”machine learning”と言ったら、それは「machine learning ではなくdeep learning」と突っ込まれた。ということで、deep learning と machine learningの違いについて。

Deep learning is a further development of machine learning. The major difference from conventional machine learning is that the framework used to analyze information and data is different. This is a “neural network” created by imitating human nerves, making computer analysis and learning of data powerful.

Although there are AI mechanisms for “machine learning” and “deep learning”, it can be said that there is a difference that automation of functional enhancement is being promoted. In particular, it can be said that the system is evolving in that it automatically finds out where to look for when distinguishing the object of analysis.