Deep learning is having a large impact on the field of natural language processing. But, as a beginner, where do you start? Both deep learning and natural language processing are huge fields. What are the salient aspects of each field to focus on and which areas of NLP is deep learning having the most impact? In this post, you will discover a primer on deep learning for natural language processing.
Existing deep learning models may encounter great challenges in handling graph structured data. In this paper, we introduce a new deep learning model for graph data specifically, namely the deep loopy neural network. Significantly different from the previous deep models, inside the deep loopy neural network, there exist a large number of loops created by the extensive connections among nodes in the input graph data, which makes model learning an infeasible task. To resolve such a problem, in this paper, we will introduce a new learning algorithm for the deep loopy neural network specifically. Instead of learning the model variables based on the original model, in the proposed learning algorithm, errors will be back-propagated through the edges in a group of extracted spanning trees. Extensive numerical experiments have been done on several real-world graph datasets, and the experimental results demonstrate the effectiveness of both the proposed model and the learning algorithm in handling graph data.
Learn Artificial Neural Networks (ANN) in R. Build predictive deep learning models using Keras and Tensorflow R Studio R interface to Keras Keras is a high-level neural networks API developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research. Keras has the following key features: Allows the same code to run on CPU or on GPU, seamlessly. User-friendly API which makes it easy to quickly prototype deep learning models. This means that Keras is appropriate for building essentially any deep learning model, from a memory network to a neural Turing machine.
On December 14, 2018, IBM released NeuNetS, a fundamentally new capability that addresses the skills gap for the development of latest AI models for a wide range of business domains. NeuNetS uses AI to automatically synthesize deep neural network models faster and easier than ever before, scaling up the adoption of AI by companies and SMEs. By fully automating AI model development and deployment, NeuNetS allows non-expert users to build neural networks for specific tasks and datasets in a fraction of the time it takes today--without sacrificing accuracy. AI is changing the way businesses work and innovate. Artificial neural networks are arguably the most powerful tool currently available to data scientists.