Inspired by the biological neural networks, this computing system "learns" to perform various tasks by taking into consideration certain examples, usually without being programmed with rules which are task-specific. Neural networks are a functional unit of deep learning and are inspired by the structure of the human brain. However, the more recent Artificial neural networks are functional unit of deep learning. The computing systems might learn to identify images that contain cats by analyzing example images that have been manually labeled as "cat" or "no cat". By using the results to identify cats in other images, they are able to learn to identify the actual image.
Autoencoders are a deep learning model for transforming data from a high-dimensional space to a lower-dimensional space. They work by encoding the data, whatever its size, to a 1-D vector. This vector can then be decoded to reconstruct the original data (in this case, an image). The more accurate the autoencoder, the closer the generated data is to the original. In this tutorial we'll explore the autoencoder architecture and see how we can apply this model to compress images from the MNIST dataset using TensorFlow and Keras.
Most people are familiar with building sequential models, in which layers follow each other one by one. For instance, in a convolutional neural network, we may decide to pass images through a convolutional layer, a max pooling layer, a flattening layer, then a dense layer. These standard constructions of networks are known as'linear topologies'. However, many high-performing networks are not linear topologies, for example the Inception module, core to the top Inception model. In the module, an input from one layer is passed into four separate layers, which are concatenated back into one output layer.
A novel kernel-based support vector machine (SVM) for graph classification is proposed. The SVM feature space mapping consists of a sequence of graph convolutional layers, which generates a vector space representation for each vertex, followed by a pooling layer which generates a reproducing kernel Hilbert space (RKHS) representation for the graph. The use of a RKHS offers the ability to implicitly operate in this space using a kernel function without the computational complexity of explicitly mapping into it. The proposed model is trained in a supervised end-to-end manner whereby the convolutional layers, the kernel function and SVM parameters are jointly optimized with respect to a regularized classification loss. This approach is distinct from existing kernel-based graph classification models which instead either use feature engineering or unsupervised learning to define the kernel function. Experimental results demonstrate that the proposed model outperforms existing deep learning baseline models on a number of datasets.
Graph theory is the study of graphs, mathematical structures that model the relationships between objects. In this example, we see a social network. A line represents a friendship between the people that it connects. In more technical terms, every person would be called a "node" or "vertex," while every line that connects would be called a "link" or "edge." So, this graph has 5 vertices and 7 edges.