기고글
Deep Neural Networks (Part VIII). Increasing the classification quality of bagging ensembles MetaTrader 5를 위하여
The article considers three methods which can be used to increase the classification quality of bagging ensembles, and their efficiency is estimated. The effects of optimization of the ELM neural network hyperparameters and postprocessing parameters are evaluated
Deep Neural Networks (Part VII). Ensemble of neural networks: stacking MetaTrader 5를 위하여
We continue to build ensembles. This time, the bagging ensemble created earlier will be supplemented with a trainable combiner — a deep neural network. One neural network combines the 7 best ensemble outputs after pruning. The second one takes all 500 outputs of the ensemble as input, prunes and
Deep Neural Networks (Part VI). Ensemble of neural network classifiers: bagging MetaTrader 5를 위하여
The article discusses the methods for building and training ensembles of neural networks with bagging structure. It also determines the peculiarities of hyperparameter optimization for individual neural network classifiers that make up the ensemble. The quality of the optimized neural network
Deep Neural Networks (Part V). Bayesian optimization of DNN hyperparameters MetaTrader 5를 위하여
The article considers the possibility to apply Bayesian optimization to hyperparameters of deep neural networks, obtained by various training variants. The classification quality of a DNN with the optimal hyperparameters in different training variants is compared. Depth of effectiveness of the DNN
Deep Neural Networks (Part IV). Creating, training and testing a model of neural network MetaTrader 5를 위하여
This article considers new capabilities of the darch package (v.0.12.0). It contains a description of training of a deep neural networks with different data types, different structure and training sequence. Training results are included
Deep Neural Networks (Part III). Sample selection and dimensionality reduction MetaTrader 5를 위하여
This article is a continuation of the series of articles about deep neural networks. Here we will consider selecting samples (removing noise), reducing the dimensionality of input data and dividing the data set into the train/val/test sets during data preparation for training the neural network
Deep Neural Networks (Part II). Working out and selecting predictors MetaTrader 5를 위하여
The second article of the series about deep neural networks will consider the transformation and choice of predictors during the process of preparing data for training a model
Deep Neural Networks (Part I). Preparing Data MetaTrader 5를 위하여
This series of articles continues exploring deep neural networks (DNN), which are used in many application areas including trading. Here new dimensions of this theme will be explored along with testing of new methods and ideas using practical experiments. The first article of the series is dedicated
Self-optimization of EA: Evolutionary and genetic algorithms MetaTrader 5를 위하여
This article covers the main principles set fourth in evolutionary algorithms, their variety and features. We will conduct an experiment with a simple Expert Advisor used as an example to show how our trading system benefits from optimization. We will consider software programs that implement
Deep neural network with Stacked RBM. Self-training, self-control MetaTrader 4를 위하여
This article is a continuation of previous articles on deep neural network and predictor selection. Here we will cover features of a neural network initiated by Stacked RBM, and its implementation in the "darch" package
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금이 들어간 이 Hochma는 어떠세요?
미국은 귀금속 시세를 중단하고 공식 달러 환율을 수정하기 위해 준비하고 있습니다 29.12.14 17:10 경제 출처: Les États-Unis préparent la fin de la cotation des métaux précieux et verrouillent le cours officiel du Dollar 2014년 12월 22일부터 사실상 간섭이 없는 귀금속 가격 변동이 미국 시장에서 엄격하게 제한됩니다. 결과적으로 금으로 표현된 달러의 가치는 공식적으로 일정한 가치가 되지만 실제로는 어떤 산의 달러로도 1온스의 금을