In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

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. Keywords—machine learning; deep learning; TensorFlow; Deep Learning4j; H2O; MNIST; multicore CPU; GPU.
Comparative Analysis of Open Source Frameworks for Machine Learning with Use Case in Single- Threaded and Multi-Threaded Modes

In this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gated recurrent unit (GRU). We evaluate these recurrent units on the tasks of polyphonic music modeling and speech signal modeling. Our experiments revealed that these advanced recurrent units are indeed better than more traditional recurrent units such as tanh units. Also, we found GRU to be comparable to LSTM.
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

We introduce a novel latent vector space model that jointly learns<lb>the latent representations of words, e-commerce products and a<lb>mapping between the two without the need for explicit annotations.<lb>The power of the model lies in its ability to directly model the dis-<lb>criminative relation between products and a particular word. We<lb>compare our method to existing latent vector space models (LSI,<lb>LDA and word2vec) and evaluate it as a feature in a learning to<lb>rank setting. Our latent vector space model achieves its enhanced<lb>performance as it learns better product representations. Further-<lb>more, the mapping from words to products and the representations<lb>of words benefit directly from the errors propagated back from the<lb>product representations during parameter estimation. We provide<lb>an in-depth analysis of the performance of our model and analyze<lb>the structure of the learned representations.

Learning Latent Vector Spaces for Product Search