Towards Reproducible Descriptions of Neuronal Network Models Introduction Science advances human knowledge through learned discourse based on mutual criticism of ideas and observations. This discourse depends on the unambiguous specification of hypotheses and experimental procedures—otherwise any criticism could be diverted easily. Moreover, communication among scientists will be effective only if a publication evokes in a reader the same ideas as the author had in mind upon writing . Scientific disciplines have over time developed a range of abstract notations, specific terminologies and common practices for describing methods and results. Matrix notation provides an illustrative example of the power of notation. (1)Defining and , etc., we can write this more compactly as (2)Introducing matrix notation simplifies this further to (3)with multiple advantages: the equation is much more compact, since the summing operation is hidden, as well as the system size; most importantly, the equation is essentially reduced to a simple multiplication. , and .
Swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems. The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Example algorithms Particle swarm optimization Ant colony optimization Artificial bee colony algorithm Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005, and simulates the foraging behaviour of honey bees. Bacterial colony optimization Differential evolution Differential evolution is similar to genetic algorithm and pattern search. The bees algorithm Artificial immune systems Bat algorithm
GUESS: The Graph Exploration System Visualizing the stock market structure This example employs several unsupervised learning techniques to extract the stock market structure from variations in historical quotes. The quantity that we use is the daily variation in quote price: quotes that are linked tend to cofluctuate during a day. Learning a graph structure We use sparse inverse covariance estimation to find which quotes are correlated conditionally on the others. Specifically, sparse inverse covariance gives us a graph, that is a list of connection. For each symbol, the symbols that it is connected too are those useful to explain its fluctuations. Clustering We use clustering to group together quotes that behave similarly. Note that this gives us a different indication than the graph, as the graph reflects conditional relations between variables, while the clustering reflects marginal properties: variables clustered together can be considered as having a similar impact at the level of the full stock market. Embedding in 2D space Visualization Script output:
BMII: Brain Machine Interfacing Initiative More low-level w/ great visuals The project describes teaching process of multi-layer neural network employing backpropagation algorithm. To illustrate this process the three layer neural network with two inputs and one output,which is shown in the picture below, is used: Each neuron is composed of two units. First unit adds products of weights coefficients and input signals. The second unit realise nonlinear function, called neuron activation function. Signal e is adder output signal, and y = f(e) is output signal of nonlinear element. To teach the neural network we need training data set. Propagation of signals through the hidden layer. Propagation of signals through the output layer. In the next algorithm step the output signal of the network y is compared with the desired output value (the target), which is found in training data set. It is impossible to compute error signal for internal neurons directly, because output values of these neurons are unknown. Coefficient h affects network teaching speed.
machine learning in Python "We use scikit-learn to support leading-edge basic research [...]" "I think it's the most well-designed ML package I've seen so far." "scikit-learn's ease-of-use, performance and overall variety of algorithms implemented has proved invaluable [...]." "For these tasks, we relied on the excellent scikit-learn package for Python." "The great benefit of scikit-learn is its fast learning curve [...]" "It allows us to do AWesome stuff we would not otherwise accomplish" "scikit-learn makes doing advanced analysis in Python accessible to anyone." python - Correcting matplotlib colorbar ticks Pourquoi le machine learning cartonne dans la Silicon Valley Pourquoi et comment Facebook ou Google se servent de l'apprentissage automatique ? Peut-il servir à d'autres acteurs ? Des techniques pas nouvelles, mais en plein boom. L'apprentissage automatique ou "machine learning" est en vogue dans la Silicon Valley. Récemment, Facebook a expliqué s'en être servi pour mettre en avant plus de contenu de haute qualité dans le flux d'actualités proposé à ses utilisateurs. Il est aussi apparu assez clairement que le moteur de recherche de Google s'était largement appuyé sur le machine learning pour mettre au point son algorithme Google Panda, lui aussi chargé de mieux mettre en avant des sites web de qualité. Le sondage souvent utilisé comme base du machine learning Dans les deux cas, Google et Facebook ont conçu, dans le cadre d'un sondage, une série de questions qui devaient servir à déterminer la qualité d'un contenu. Sondage, arbre de décision : des éléments que l'on retrouve souvent dans le machine learning. De vastes champs d'applications
Neurons w/ Python Neurons, as an Extension of the Perceptron Model In a previous post in this series we investigated the Perceptron model for determining whether some data was linearly separable. That is, given a data set where the points are labelled in one of two classes, we were interested in finding a hyperplane that separates the classes. In the case of points in the plane, this just reduced to finding lines which separated the points like this: A hyperplane (the slanted line) separating the blue data points (class -1) from the red data points (class +1) As we saw last time, the Perceptron model is particularly bad at learning data. Use a number of Perceptron models in some sort of conjunction.Use the Perceptron model on some non-linear transformation of the data. The point of both of these is to introduce some sort of non-linearity into the decision boundary. . is in, we check the sign of an inner product with an added constant shifting term: . have weights Definition: A function and the sigmoid curve .