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100-days-of-ml-code

100-Days-Of-ML-Code中文版

3dmpm

Learning 3D Mineral Prospectivity from 3D Geological Models Using Convolutional Neural Networks

a-hybrid-deep-learning-based-model-for-volatility-prediction

Developing hybrid deep learning models by integrating Neural networks with (s,e,t)GARCH models to predict volatility in the Indian Commodity Market. We evaluate the following DNN models: Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and RNN with Long-Short Term Memory (LSTM-RNN) and RNN with Gated-Recurrent Unit (GRU-RNN).

advanced-deep-learning-with-keras

Advanced Deep Learning with Keras, published by Packt

ai-edu

AI education materials for Chinese students, teachers and IT professionals.

an-introduction-to-statistical-learning

This repository contains the exercises and its solution contained in the book "An Introduction to Statistical Learning" in python.

anomaly-detection

geological guided adversarial learning model for anomaly detection

autolisp

autolisp-1

Old custom AutoLISP code for AutoCAD.

autolisp-2

beautiful-visualization-with-python

python数据可视化之美

blog

Python机器学习算法技术博客，有原创干货！有code实践！

clm-lstm-soil-moisture-prediction

Explored the Long-Term Soil Moisture Predictability with FLUXNET Site Data using Circulating Learning Model

cnn-lstm-for-gold-price_forecasting

Implemented a hybrid neural network using convolutional neural network and recurrent neural network to predict gold prices

cnn-lstm-network-architecture-for-geological-mapping

Application of convolutional recurrent neural networks for geological feature mapping based on geochemical exploration data.

cnn_fe

cpython

The Python programming language

data-analysis-with-r

dbn

Simple code tutorial for deep belief network (DBN)

dbn_predict_performance

graduation design DBN + SVM

dbn_tf

RBM/DBN implementation with tensorflow attempt project

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deep_learning_note

This repository provides in detail common machine learning models such as Neural Network ,SVM,Lasso,Ridge,KNN,Perception and Naive Bayes, which are derived manually using basic Python libraries such as numpy pandas and encapsulated these models by a class

deepbelief

Code for training and evaluating restricted Boltzmann machines (RBMs) and deep belief networks (DBNs).

deeplearning

doing-meta-analysis-in-r

All R code and source files for the online guide "Doing Meta-Analysis with R: A Hands-On Guide"

fcode

feconv

Program feconv is an utility to convert between several mesh and FE field formats, like ANSYS mesh files (.msh), MD Nastran input files (.bdf), I-Deas Universal (.unv), VTK files (.vtk), etc. If you use a Modulef format to store an intermediate mesh (.mfm). It can transform the FE type of a mesh composed of trangles or tetrahedra, to Lagrange P1 or P2, Raviart-Thomas (face) or Whitney (edge) finite elements. It also uses third-party code to perform bandwidth optimization (CutlHill-McKee optimization).

finding-models-of-heat-conduction-via-machine-learning

flap

Fortran command Line Arguments Parser for poor people