This application uses a fully connected neural network with one hidden layer described by Michael Nielsen research paper.Together with input and output layers the network constitutes three layers.

We propose a soft attention based model for the task of action recognition in videos. We use multi-layered Recurrent Neural Networks (RNNs) with Long-Short Term Memory (LSTM) units which are deep both ...

The team of Cambridge-based researchers has investigated object recognition processes using a new method that combines deep neural networks with an attractor network model of semantics. In contrast ...

Fine-Grained Image Recognition: The process of distinguishing closely related subcategories within a broader category by analysing subtle and discriminative visual features. Convolutional Neural ...

Deep neural networks trained on large annotated visual datasets have become state-of-the-art models for both visual recognition tasks and predicting neuronal responses in the primate visual stream.

Summary: Researchers developed a tool that simplifies the identification of errors in neural networks used for image recognition. Neural networks often produce errors that are challenging to trace, ...

Audio-visual speech recognition is a promising approach to tackling the problem of reduced recognition rates under adverse acoustic conditions. However, finding an optimal mechanism for combining ...

The use of neural networks is becoming increasingly widespread in many areas of human activity. Thus, artificial intelligence technologies are being introduced into many mechanisms for ensuring ...

There are 50 data points for each species. Because neural networks work with numeric data, the categorical species information must be converted to numeric data. When performing neural network ...

Even if you never write neural network code, understanding exactly how back-propagation works will enable you to use neural network tools more effectively. Finally, you may find back-propagation ...