地质领域机器学习、深度学习及实现语言

地质大数据正在以指数形式增长。只有发展智能数据处理方法才有可能追上大数据的超常增长。机器学习是人工智能的核心,是使计算机具有智能的根本途径。机器学习已成为地质大数据研究的前沿热点,它将让地质大数据插上翅膀,并因此改变地质。机器学习是一个源于数据的模型的训练过程,最终给出一个面向某种性能度量的决策。深度学习是机器学习研究中的一个重要子类,它通过构建具有很多隐层的机器学习模型和海量的训练数据,来学习更有用的特征,从而最终提升分类或预测的准确性。卷积神经网络算法是最为常用的一种深度学习算法之一,它广泛用于图像识别和语音分析等。Python语言在科学领域的地位占据着越来越重要。其下的Scikit-Learn是一个机器学习相关的库,提供有数据预处理、分类、回归、聚类、预测、模型分析等算法。Keras是一个基于Theano/Tensorflow的深度学习库,可以应用来搭建简洁的人工神经网络。

Machine learning, deep learning and Python language in field of geology

Geological big data is exponentially expanding. It is the only way to catch up with its extraordinary growing to develop intelligent data processing. As the core of artificial intelligence, machine learning is a fundamental way to endow computer with intelligence. Machine learning has been becoming the front hotspot of geological big data mining. It will attach wings to geological big data mining, and thereby bring revolution to geological research. Machine learning is a data adaptive training process and model, resulting in giving a good performance decision. As a subclass of machine learning, deep learning develops machine learning model with various hidden layers, and makes iterative evolution of the model through massive data training, and finally extracts essential features to help more exactly classing and predicting. The convolution neural network is one of the most frequently used deep learning algorithms. It is widely used in image recognition and speech analysis. Python language is playing an increasingly important role in science research. The Python Scikit-Learn is a machine learning-oriented library to provide with data preprocessing, classification, regression, clustering, prediction, model analysis and other modules. The Keras is a Python deep learning library based on Theano and Tensorflow, and can be used to construct concise artificial neural network.