- Machine Learning in Java
- AshishSingh Bhatia Bostjan Kaluza
- 217字
- 2021-06-10 19:30:08
Deeplearning4j
Deeplearning4j, or DL4J, is a deep learning library written in Java. It features a distributed as well as a single-machine deep learning framework that includes and supports various neural network structures such as feedforward neural networks, RBM, convolutional neural nets, deep belief networks, autoencoders, and others. DL4J can solve distinct problems, such as identifying faces, voices, spam, or e-commerce fraud.
Deeplearning4j is also distributed under the Apache 2.0 license and can be downloaded from http://deeplearning4j.org. The library is organized as follows:
- org.deeplearning4j.base: These are loading classes
- org.deeplearning4j.berkeley: These are math utility methods
- org.deeplearning4j.clustering: This is the implementation of k-means clustering
- org.deeplearning4j.datasets: This is dataset manipulation, including import, creation, iterating, and so on
- org.deeplearning4j.distributions: These are utility methods for distributions
- org.deeplearning4j.eval: These are evaluation classes, including the confusion matrix
- org.deeplearning4j.exceptions: This implements the exception handlers
- org.deeplearning4j.models: These are supervised learning algorithms, including deep belief networks, stacked autoencoders, stacked denoising autoencoders, and RBM
- org.deeplearning4j.nn: These are the implementations of components and algorithms based on neural networks, such as neural networks, multi-layer networks, convolutional multi-layer networks, and so on
- org.deeplearning4j.optimize: These are neural net optimization algorithms, including back propagation, multi-layer optimization, output layer optimization, and so on
- org.deeplearning4j.plot: These are various methods for rendering data
- org.deeplearning4j.rng: This is a random data generator
- org.deeplearning4j.util: These are helper and utility methods
推薦閱讀
- ROS機器人編程與SLAM算法解析指南
- JBoss ESB Beginner’s Guide
- Apache Spark Deep Learning Cookbook
- Photoshop CS3圖像處理融會貫通
- SAP Business Intelligence Quick Start Guide
- Dreamweaver CS6精彩網頁制作與網站建設
- Mastering GitLab 12
- Mastering Geospatial Analysis with Python
- 21天學通Linux嵌入式開發
- 工業機器人入門實用教程
- Machine Learning with Spark(Second Edition)
- 實戰Windows Azure
- 網管員世界2009超值精華本
- 大數據時代的調查師
- 軟件測試設計