The Java and Scala community has built a very successful big data ecosystem.
However, most of neural networks running on it are modeled in dynamically typed
programming languages. These dynamically typed deep learning frameworks treat
neural networks as differentiable expressions that contain many trainable
variable, and perform automatic differentiation on those expressions when
training them.
Until 2019, none of the learning frameworks in statically typed languages
provided the expressive power of traditional frameworks. Their users are not
able to use custom algorithms unless creating plenty of boilerplate code for
hard-coded back-propagation.
We solved this problem in DeepLearning.scala 2. Our contributions are:
1. We discovered a novel approach to perform automatic differentiation in
reverse mode for statically typed functions that contain multiple trainable
variable, and can interoperate freely with the metalanguage.
2. We designed a set of monads and monad transformers, which allow users to
create monadic expressions that represent dynamic neural networks.
3. Along with these monads, we provide some applicative functors, to perform
multiple calculations in parallel.
With these features, users of DeepLearning.scala were able to create complex
neural networks in an intuitive and concise way, and still maintain type
safety.

使用 Scala 语言的 DeepLearning.scala 框架提供了一种创新的自动微分方法，允许用户以直观简洁的方式创建复杂的神经网络，并保持类型安全。

单子化深度学习

Monadic Deep Learning

The calibration of the EFL teaching and learning approaches with Artificial
Intelligence can potentially facilitate a smart transformation, fostering a
personalized and engaging experience in teaching and learning among the
stakeholders. The paper focuses on developing an EFL Big Data Ecosystem that is
based on Big Data, Analytics, Machine Learning and cluster domain of EFL
teaching and learning contents. Accordingly, the paper uses two membranes to
construe its framework, namely (i) Open Big Data Membrane that stores random
data collected from various source domains and (ii) Machine Learning Membrane
that stores specially prepared structured and semi-structured data.
Theoretically, the structured and semi structured data are to be prepared
skill-wise, attribute-wise, method-wise, and preference-wise to accommodate the
personalized preferences and diverse teaching and learning needs of different
individuals. The ultimate goal is to optimize the learning experience by
leveraging machine learning to create tailored content that aligns with the
diverse teaching and learning needs of the EFL communities.

本文介绍了使用人工智能技术进行英语外语教学的重要性，并应用大数据、机器学习和聚类域的教学学习内容来开发 EFL 大数据生态系统，以提供个性化、吸引人的教学和学习体验。