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Introduction to Neural Networks

Introduction to Neural Networks
CS-449: Neural Networks Fall 99 Instructor: Genevieve Orr Willamette University Lecture Notes prepared by Genevieve Orr, Nici Schraudolph, and Fred Cummins [Content][Links] Course content Summary Our goal is to introduce students to a powerful class of model, the Neural Network. We then introduce one kind of network in detail: the feedforward network trained by backpropagation of error. Lecture 1: Introduction Lecture 2: Classification Lecture 3: Optimizing Linear Networks Lecture 4: The Backprop Toolbox Lecture 5: Unsupervised Learning Lecture 6: Reinforcement Learning Lecture 7: Advanced Topics [Top] Review for Midterm: Links Tutorials: The Nervous System - a very nice introduction, many pictures Neural Java - a neural network tutorial with Java applets Web Sim - A Java neural network simulator. a book chapter describing the Backpropagation Algorithm (Postscript) A short set of pages showing how a simple backprop net learns to recognize the digits 0-9, with C code Reinforcement Learning - A Tutorial Related:  Neural Network

Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. After being weighted and transformed by a function (determined by the network's designer), the activations of these neurons are then passed on to other neurons. This process is repeated until finally, an output neuron is activated. This determines which character was read. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] History[edit] Farley and Wesley A. Models[edit] or both

Laboratory Fundamentals of Synthetic Biology From OpenWetWare Syllabus Class Format The Class will meet twice a week, one 2 hour classroom session, and one 3 hour lab session. Problem sets will be assigned weekly for the first eight weeks. Grades The final grade will be as follows: 20% Problem Sets 30% Midterm Exam 20% Lab Evaluations 30% Final Project Schedule Introduction - Synthetic Biology: History, current applications and future directions Powerpoint (w/content from Drew Endy) Assignments: Endy Article and Comic Strip The Biology (4 sessions) Cells, DNA, RNA and Protein DNA - information encoding, structure, sequencing and synthesis RNA - encoding, structure, function (RNA Enzymes, RNA Aptamers) Proteins - Crystallography, functions, scaffolds Introductory packet about DNA, RNA, Protein. Create a biobrick out of a sequence (force them to re-optimize a coding region into e. coli and remove a biobrick incompatibility). Reading List This work is licensed under a Creative Commons Attribution-Share Alike 3.0 License.

Neural Networks Abstract This report is an introduction to Artificial Neural Networks. The various types of neural networks are explained and demonstrated, applications of neural networks like ANNs in medicine are described, and a detailed historical background is provided. The connection between the artificial and the real thing is also investigated and explained. Contents: 1. 1.1 What is a neural network? 1.2 Historical background 1.3 Why use neural networks? 1.4 Neural networks versus conventional computers - a comparison 2. 2.1 How the Human Brain Learns? 2.2 From Human Neurones to Artificial Neurones 3. 3.1 A simple neuron - description of a simple neuron 3.2 Firing rules - How neurones make decisions 3.3 Pattern recognition - an example 3.4 A more complicated neuron 4. 4.1 Feed-forward (associative) networks 4.2 Feedback (autoassociative) networks 4.3 Network layers 4.4 Perceptrons 5. 5.1 Transfer Function 5.2 An Example to illustrate the above teaching procedure 5.3 The Back-Propagation Algorithm 6. 7. 1. 2. 3.

brains in silicon Welcome to Brains in Silicon. Learn about the lab, get to know the brains that work here, and find out about new projects that you could join. We have crafted two complementary objectives: To use existing knowledge of brain function in designing an affordable supercomputer—one that can itself serve as a tool to investigate brain function—feeding back and contributing to a fundamental, biological understanding of how the brain works. We model brains using an approach far more efficient than software simulation: We emulate the flow of ions directly with the flow of electrons—don't worry, on the outside it looks just like software. Welcome and enjoy your time here!

What is neural network? - Definition from Whatis In information technology, a neural network is a system of programs and data structures that approximates the operation of the human brain. A neural network usually involves a large number of processors operating in parallel, each with its own small sphere of knowledge and access to data in its local memory. Typically, a neural network is initially "trained" or fed large amounts of data and rules about data relationships (for example, "A grandfather is older than a person's father"). In making determinations, neural networks use several principles, including gradient-based training, fuzzy logic, genetic algorithms, and Bayesian methods. Current applications of neural networks include: oil exploration data analysis, weather prediction, the interpretation of nucleotide sequences in biology labs, and the exploration of models of thinking and consciousness. Contributor(s): Lee Giles This was last updated in July 2006 Email Alerts

Synbio 2007 From OpenWetWare General Info Spring 2007 Instructor: Jay Keasling (keasling@berkeley.edu) GSI: Jeffrey Dietrich (jadietrich@gmail.com) Logistics: Lecture/Discussion: 2 hours, 10-12 AM Friday Grading: Literature Review 30% Group Project 60% Class Participation 10% Office hours: contact Jeffrey Dietrich to arrange a meeting Announcements ASSIGNMENT (Due 2/16): email Jeff with your three top choices for topics to lead in literature review group discussion. Tentative Schedule 1/19 Introduction, Basis for Synthetic Biology - Jay Keasling 1/26 Modeling and Design of Synthetic Systems - Adam Arkin Genetic models, stochastic and continuous simulations, adaption of circuit methods to SB. 2/2 Drugs from Bugs-Jay Keasling 2/9 Design of Tumor-Killing Bacteria - J. Literature Review Assignment Every student will be required to lead one class discussion over selected readings/topics assigned for that week. Group Project Ideas Group projects from 2007 (Presentations and References) a. b. c. d. Policy Approach

Neural Network Toolbox - MATLAB Neural Network Toolbox™ provides functions and apps for modeling complex nonlinear systems that are not easily modeled with a closed-form equation. Neural Network Toolbox supports supervised learning with feedforward, radial basis, and dynamic networks. It also supports unsupervised learning with self-organizing maps and competitive layers. With the toolbox you can design, train, visualize, and simulate neural networks. You can use Neural Network Toolbox for applications such as data fitting, pattern recognition, clustering, time-series prediction, and dynamic system modeling and control. To speed up training and handle large data sets, you can distribute computations and data across multicore processors, GPUs, and computer clusters using Parallel Computing Toolbox™.

The Scala Programming Language NeuroSolutions: What is a Neural Network? What is a Neural Network? A neural network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain. Neural networks resemble the human brain in the following two ways: A neural network acquires knowledge through learning. A neural network's knowledge is stored within inter-neuron connection strengths known as synaptic weights. The true power and advantage of neural networks lies in their ability to represent both linear and non-linear relationships and in their ability to learn these relationships directly from the data being modeled. The most common neural network model is the multilayer perceptron (MLP). Block diagram of a two hidden layer multiplayer perceptron (MLP).

Music and Colour ( Color ): a new approach to the relationship Researchers Create Artificial Neural Network from DNA 5inShare Scientists at the California Institute of Technology (Caltech) have successfully created an artificial neural network using DNA molecules that is capable of brain-like behavior. Hailing it as a “major step toward creating artificial intelligence,” the scientists report that, similar to a brain, the network can retrieve memories based on incomplete patterns. Potential applications of such artificially intelligent biochemical networks with decision-making skills include medicine and biological research. More details from Caltech: Consisting of four artificial neurons made from 112 distinct DNA strands, the researchers’ neural network plays a mind-reading game in which it tries to identify a mystery scientist. Check out these YouTube videos describing the research: Full story: Caltech researchers create the first artificial neural network out of DNA…

Neural network gets an idea of number without counting - tech - 20 January 2012 AN ARTIFICIAL brain has taught itself to estimate the number of objects in an image without actually counting them, emulating abilities displayed by some animals including lions and fish, as well as humans. Because the model was not preprogrammed with numerical capabilities, the feat suggests that this skill emerges due to general learning processes rather than number-specific mechanisms. "It answers the question of how numerosity emerges without teaching anything about numbers in the first place," says Marco Zorzi at the University of Padua in Italy, who led the work. The finding may also help us to understand dyscalculia - where people find it nearly impossible to acquire basic number and arithmetic skills - and enhance robotics and computer vision. The skill in question is known as approximate number sense. A simple test of ANS involves looking at two groups of dots on a page and intuitively knowing which has more dots, even though you have not counted them. More From New Scientist

Introduction aux Réseaux de Neurones Artificiels Feed Forward Plongeons-nous dans l'univers de la reconnaissance de formes. Plus particulièrement, nous allons nous intéresser à la reconnaissance des chiffres (0, 1, ..., 9). Imaginons un programme qui devrait reconnaître, depuis une image, un chiffre. On présente donc au programme une image d'un "1" manuscrit par exemple et lui doit pouvoir nous dire "c'est un 1". De façon plus générale, un réseau de neurone permet l'approximation d'une fonction. Dans la suite de l'article, on notera un vecteur dont les composantes sont les n informations concernant un exemple donné. Voyons maintenant d'où vient la théorie des réseaux de neurones artificiels. Comment l'homme fait-il pour raisonner, parler, calculer, apprendre... ? Approches adoptée en recherche en Intelligence Artificielle procéder d'abord à l'analyse logique des tâches relevant de la cognition humaine et tenter de les reconstituer par programme. La physiologie du cerveau montre que celui-ci est constitué de cellules (les neurones) interconnectées.

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