A neural network is a series of algorithms that endeavors to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. In this sense, neural networks refer to systems of neurons, either organic or artificial in nature.
Radial Basis Function(RBF) network is an artificial neural network that uses radial basis functions as activation functions. The output of the network is a linear combination of radial basis functions of the inputs and neuron parameters. Radial basis function networks have many uses, including function approximation, time series prediction, classification, and system control. They were first formulated in a 1988 … Read more
Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that provide a little power to classify instances. Pruning reduces the complexity of the final classifier, and hence improves predictive accuracy by the reduction of overfitting. One of the questions that arise in a decision tree algorithm is the optimal … Read more
Feedforward Neural Network (FNN) is a biologically inspired classification algorithm. It consists of a (possibly large) number of simple neuron-like processing units, organized in layers. Every unit in a layer is connected with units in the previous layer. These connections are not all equal: each connection may have a different strength or weight. The weights on these connections encode the … Read more
Deep Learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a class of machine learning algorithms that: use a cascade of many layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The algorithms may be supervised or unsupervised and applications include pattern … Read more
Deep Belief Nets are probabilistic generative models that are composed of multiple layers of stochastic, latent variables. The latent variables typically have binary values and are often called hidden units or feature detectors. The top two layers have undirected, symmetric connections between them and form an associative memory. The lower layers receive top-down, directed connections from the layer above. The … Read more
Convolutional Neural Network (CNN) is made up of neurons that have learnable weights and biases. CNN are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars. CNNs transform the original image … Read more
Boltzmann machine is a network of symmetrically connected, neuronlike units that make stochastic decisions about whether to be on or off. Boltzmann machines have a simple learning algorithm that allows them to discover interesting features in datasets composed of binary vectors. The learning algorithm is very slow in networks with many layers of feature detectors, but it can be made … Read more
In a previous post on Numerai, I have described very basic code to get into a world of machine learning competitions. This one will be a continuation, so if you haven’t read it I recommend to do it- here. In this post, we will add little more complexity to the whole process. We will split out 20% of training data … Read more
What is Neural Network? Definition Artificial Neural network is a system of individual processing units (Neurons) connected usually in a structured manner. It is designed to mimic operation and model the way human brain solves problems. Each individual neural unit has a function which processes the values of all its inputs. The organization and weights of connections determine the output of the … Read more