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What Is A Neural Network?
The simplest definition of a neural network, more properly referred to as an ‘artificial’ neural network (ANN), is provided by the inventor of one of the first neurocomputers, Dr. Robert Hecht-Nielsen. He defines a neural network as:
…a computing system made up of a number of simple, highly interconnected processing elements, which process information by their dynamic state response to external inputs.
The Fundamentals of Neural Networks
Neural neworks are normally sorted out in layers. Layers are made up of a number of interconnected ‘nodes’ which contain an ‘initiation work’. Patterns are presented to the network via the ‘input layer’, which communicates to one or more ‘hidden layers’ where the actual processing is done via a system of weighted ‘connections’. The hidden layers then link to an ‘output layer’ where the answer is output as shown in the graphic below.
Most ANNs contain some form of ‘learning standard’ which modifies the weights of the connections according to the input patterns that it is presented with. In a sense, ANNs learn by example as do their biological counterparts; a child learns to recognize dogs from examples of dogs.
Although there are many different kinds of learning rules used by neural networks, this demonstration is concerned only with one; the delta rule. The delta rule is often utilized by the most common class of ANNs called ‘backpropagational neural networks’ (BPNNs).
With the delta rule, as with other types of backpropagation, ‘learning’ is a supervised process that occurs with each cycle or ‘epoch‘ (i.e. each time the network is presented with a new input pattern) through a forward activation flow of outputs, and the backwards error propagation of weight adjustments.
Top 50 Best YouTube Videos on Neural Networks until 2017.
Introduction video about Neural Networks, a plugin in OpendTect. The demo is given by dGB Earth Sciences’ Marieke de Groot.
Neural Networks, A Simple Explanation
Neural Networks are about computers simulating biological neurons and the way they process information.
This short animated video does not show how Neural Networks learn and it does not show an in depth explanation of the math behind Neural Networks. Instead it is meant to provide most people with an easy to understand format regarding the inner workings of Neural Networks and how they process inputs/information into outputs/results.
Here is a Complete Video Playlist by Instructor Hugo Larochelle which contains 92 Videos series along with the slides and research paper references on Neural Network Class.
Videos are very short and clear to understand the concepts for every beginner learning Neural networks.
Lec-1 Introduction to Artificial Neural Networks
Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur which contains 37 Videos series.
Introduction to my project, Neural Network Lab, which is an online & interactive simulator for spiking neural networks.
Matthew Zeiler, PhD, Founder and CEO of Clarifai Inc, speaks about large convolutional neural networks. These networks have recently demonstrated impressive object recognition performance making real world applications possible. Matt covers a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the overall classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that perform exceedingly well.
In this video Geoffrey Hinton provides us talk on Neural networks at GoogleTechTalks and covers
on back propagation, digit recognition, Restricted Boltzmann Machine etc.
Neural Network Tutorial An Easy Overview Of “Neural Network” by Christopher Hunt.
In this video we can visualize activity of the bots using pre-defined set of commands.
In this video Chief Research Officer Rick Rashid demonstrates a speech recognition breakthrough via machine translation that converts his spoken English words into computer-generated Chinese language. The breakthrough is patterned after deep neural networks and significantly reduces errors in spoken as well as written translation.
Neural Network Training (Part 1): The Training Process
In this video we see how neural networks are trained. This part overviews the training process.
Introduction to Neural Network by Pof B Yegnanarayana
Multi-Disciplinary International Workshop on Artificial Intelligence (MIWAI-2011)
In this video speaker shows the pattern of rethinking bacteria. Finally, the use of social networks can be seen driven by chemical tweeting.
Tutorial: How to Train a Neural Network with Azure Machine Learning
Azure Machine Learning is a new service that’s still in preview in Azure. It offers a really powerful set of tools for training neural networks, estimating statistical models, cleansing and transforming data plus a lot more.
This video demonstrates the complete process of a gene which learn to jump over the ball.
This tutorial presents a pretty novel application of ANN (Artificial Neural Network) consisting in harnessing it as a tool of portfolio hedging, uses:real data — Apple and Starbucks stocks quotes,
provides: a description of a simple hedging mechanism leading to offsetting the changes in prices of one asset portfolio by means of going additional asset position,an insight into McCulloch.
How-to use UVQ Neural Network in OpendTect 4.4. Presented by dGB’s Eric Bouanga.
Brittany Wenger, 18, high school senior, brilliant young scientist and Grand Prize Winner 2012 Google Science Fair, for her project “Global Neural Network Cloud Service for Breast Cancer” talks about how she came to science in Research and Inspiration.
Fitness indicator to those found to have the best standing posture.
Its getting better. Using 21 inputs, 1 hidden layer (20 neurons) and 12 outputs as rotations.
They do seem to be getting the hang of it.
This video explains how to input the collected data into the Neural Network engine
The MetaNeural EA works with Metatrader 4 and can create, train, test, and use cutting-edge neural networks for automated trading.
This video is an introduction to artificial neural networks. It was made by high school student Dean Young as part of an assignment for “Introduction to Cognitive Neuroscience” taught in the Winter of 2012.
In this video, a genetic algorithm learn how to fight.
This course teaches the foundation of neural network models of the human visual system. The application is in synthetic and artificial vision, visual perception, visual intelligence for robots and automatic system. This course will teach how to use and write software models of the human visual system, retinal pre-processing, and vision sub-blocks. The course will teach machine- and deep-learning neural networks system to learn to segment, track, categorize, classify, objects of interest in the scene.
The course will also focus on techniques to perform full-scene understanding of a video stream, both with static and dynamic (motion) filters. We will discuss the training supervised and unsupervised of large networks for general-purpose robotic vision systems. Hardware implementation projects are also going to be a component of the course.
This course discuss the training supervised and unsupervised of large networks for general-purpose robotic vision systems. Hardware implementation projects are also going to be a component of the course.
Introduction to Neural Networks for C# Class 11 16 Part 1 5 predict stock market YouTube by Mohamed Bouzidi.
This video create’s our first binary noise trainer class. Very simple, but only our first step!
Tutorial sobre Mineração de Dados (Data Mining) utilizando software WEKA.
In this video we begin building a more robust and complete network training class.
Neural Network Tutorial – Ch. 12.1: Simple network trainer
In this video we implement the first (simple) training class and use it to train a network on XOR training data.
In this video I demo a little app I made way back in the day in which I attempted to train my computer how to balance a ball. It’s kinda cool and is interactive and animated so you can really see the network evolve over time for different input.
In this video we train a back propagation network to do some simple curve fitting, not using discrete data points as source, but instead generating them from a function. Conceptually, this is easily extensible to training a network to learn something based on user input, or based on a simulation, which itself may be quite complex, but whose solution may be approximated by the network.
In this tutorial we simply run through a complete (though simple) example of training a 2-2-1 network to learn the XOR-Gate.
In this video we will begin our Load method for the back propagation network, which will open an XML file, and turn it back into a neural network!
In this video we will create a Save method which saves the network to XML!
We will look at and add linear, gaussian, and rational (sigmoid-esque) functions and test them in our network test program.
In this video we will begin developing the Train method for our back propagation library.
In this chapter we will build the run method for propagating the signals forward through the network.
In this chapter we will define all necessary private variables for the network.
In this chapter we will create the transfer functions and enumerate them.
This is the first video in a series in which we will build a back propagation neural network library for C#! In this video we will set up the solution in Visual Studio to get everything ready!
In this video we will derive the back-propagation algorithm as is used for neural networks. I use the sigmoid transfer function because it is the most common, but the derivation is the same, and easily extensible.
Introduction to Neural Networks for C# (Class 16/16) Neural C# Class 16. From: Jeff Heaton
Introduction to Neural Networks for Java (Class 16/16) Neural Java Class 16. From: Jeff Heaton
In class session 5, part 1 we will look at an introduction to genetic algorithms. We will use genetic algorithms both to train a neural network and to provide a path for the traveling salesman problem.
In class session 5, part 3 we will look at how to use a genetic algorithm for the traveling salesman problem.
In class session 5, part 5 we will look at how to use a neural network and genetic algorithm to play tic tac toe (naughts and crosses).
In class session 8 we will see my solution to the mid-term. For the mid-term you created a neural network that uses data from the US forestry service to predict forest cover.
In class session 11, part 1 we will see a neural network that attempts to predict the stock market. It does this using S&P 500 data, as well as prime interest rate data.