Machine Learning, Deep Learning and Implementation Language in Geological Field

Abstract

<p class="15" align="justify">Geological big data is growing exponentially. Only by developing intelligent data processing methods can we catch up with the extraordinary growth of big data. Machine learning is the core of artificial intelligence and the fundamental way to make computers intelligent. Machine learning has become the frontier hotspot of geological big data research. It will make geological big data winged and change geology. Machine learning is a training process of model derived from data, and it eventually gives a decision oriented to a certain performance measurement. Deep learning is an important subclass of machine learning research. It learns more useful features by building machine learning models with many hidden layers and massive training data, so as to improve the accuracy of classification or prediction <span style="font-family: 'Times New Roman';">at last.</span> Convolutional neural network algorithm is one of the most commonly used deep learning algorithms. It is widely used in image recognition and speech analysis. Python language plays an increasingly important role in the field of science. Scikit-Learn is a bank related to machine learning, which provides algorithms such as data preprocessing, classification, regression, clustering, prediction and model analysis. Keras is a deep learning bank based on Theano/Tensorflow, which can be applied to build a simple artificial neural network.</p>