The basic features of some of the most versatile and popular open source frameworks for machine learning (TensorFlow, Deep Learning4j, and H2O) are considered and compared. Their comparative analysis was performed and conclusions were made as to the advantages and disadvantages of these platforms. The performance tests for the de facto standard MNIST data set were carried out on H2O framework for deep learning algorithms designed for CPU and GPU platforms for single-threaded and multithreaded modes of operation Also, we present the results of testingneural networks architectures on H2O platform for variousactivation functions, stopping metrics, and other parameters ofmachine learning algorithm. It was demonstrated for the usecase of MNIST database of handwritten digits in single-threadedmode that blind selection of these parameters can hugely increase (by 2-3 orders) the runtime without the significant increase ofprecision. This result can have crucial influence for optimizationof available and new machine learning methods, especially forimage recognition problems.