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Beginners guide to how artificial neural network model works. Learn how neural network approaches the problem, why and how the process works in ANN, various ways errors can be used in creating machine learning models and ways to optimise the learning process.
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Views: 63346
Great Learning

Analysis Of Neural Networks in Data Mining
by,
Venkatraam Balasubramanian
Master's in Industrial and Human Factor Engineering

Views: 3848
prasana sarma

More Data Mining with Weka: online course from the University of Waikato
Class 5 - Lesson 1: Simple neural networks
http://weka.waikato.ac.nz/
Slides (PDF):
http://goo.gl/rDuMqu
https://twitter.com/WekaMOOC
http://wekamooc.blogspot.co.nz/
Department of Computer Science
University of Waikato
New Zealand
http://cs.waikato.ac.nz/

Views: 20810
WekaMOOC

SSAS - Data Mining - Decision Trees, Clustering, Neural networks

Views: 778
M R Dhandhukia

PyData New York City 2017
Slides: https://github.com/llllllllll/osu-talk
Most neural network examples and tutorials use fake data or present poorly performing models. In this talk, we will walk through the process of implementing a real model, starting from the beginning with data collection and cleaning. We will cover topics like feature selection, window normalization, and feature scaling. We will also present development tips for testing and deploying models.

Views: 7232
PyData

understanding how the input flows to the output in back propagation neural network with the calculation of values in the network.
the example is taken from below link refer this https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/ for full example

Views: 26824
Naveen Kumar

In this video, I go over the 3 steps you need to prepare a dataset to be fed into a machine learning model. (selecting the data, processing it, and transforming it). The example I use is preparing a dataset of brain scans to classify whether or not someone is meditating.
The challenge for this video is here:
https://github.com/llSourcell/prepare_dataset_challenge
Carl's winning code:
https://github.com/av80r/coaster_racer_coding_challenge
Rohan's runner-up code:
https://github.com/rhnvrm/universe-coaster-racer-challenge
Come join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
Dataset sources I talked about:
https://github.com/caesar0301/awesome-public-datasets
https://www.kaggle.com/datasets
http://reddit.com/r/datasets
More learning resources:
https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-prepare-data
http://machinelearningmastery.com/how-to-prepare-data-for-machine-learning/
https://www.youtube.com/watch?v=kSslGdST2Ms
http://freecontent.manning.com/real-world-machine-learning-pre-processing-data-for-modeling/
http://docs.aws.amazon.com/machine-learning/latest/dg/step-1-download-edit-and-upload-data.html
http://paginas.fe.up.pt/~ec/files_1112/week_03_Data_Preparation.pdf
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Views: 124091
Siraj Raval

With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool!
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Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series.
Classification involves taking a set of objects and some data features that describe them, and placing them into categories. This is done by a classifier which takes the data features as input and assigns a value (typically between 0 and 1) to each object; this is called firing or activation; a high score means one class and a low score means another. There are many different types of classifiers such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes. If you have used any of these tools before, which one is your favorite? Please comment.
Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers. Each node (also called a neuron) in the hidden and output layers has a classifier. The input neurons first receive the data features of the object. After processing the data, they send their output to the first hidden layer. The hidden layer processes this output and sends the results to the next hidden layer. This continues until the data reaches the final output layer, where the output value determines the object's classification. This entire process is known as Forward Propagation, or Forward prop. The scores at the output layer determine which class a set of inputs belongs to.
Links:
Michael Nielsen's book - http://neuralnetworksanddeeplearning.com/
Andrew Ng Machine Learning - https://www.coursera.org/learn/machine-learning
Andrew Ng Deep Learning - https://www.coursera.org/specializations/deep-learning
Have you worked with neural nets before? If not, is this clear so far? Please comment.
Neural nets are sometimes called a Multilayer Perceptron or MLP. This is a little confusing since the perceptron refers to one of the original neural networks, which had limited activation capabilities. However, the term has stuck - your typical vanilla neural net is referred to as an MLP.
Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. These two numbers are changed as the neural network is trained on a set of test samples. If the accuracy is low, the weight and bias numbers are tweaked slightly until the accuracy slowly improves. Once the neural network is properly trained, its accuracy can be as high as 95%.
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Views: 355582
DeepLearning.TV

How to predict time-series data using a Recurrent Neural Network (GRU / LSTM) in TensorFlow and Keras. Demonstrated on weather-data.
https://github.com/Hvass-Labs/TensorFlow-Tutorials

Views: 15911
Hvass Laboratories

Neural network in ai (Artificial intelligence)
Neural network is highly interconnected network of a large number of processing elements called neuron architecture motivated from brain.
Neuron are interconnected to synapses which provide input from other neurons which intern provides output i.e input to other neurons.
Neuron are in massive therefore they provide distributed network.
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CaelusBot

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If you're not a data scientist, but you're interested in data mining and predictive analytics, and you want to go beyond just reporting the numbers, check out this course on Azure Machine Learning (ML). ML is the inexpensive, easy-to-access, and powerful predictive analytics offering from Microsoft.
In this demo-rich course, led by entertaining experts Buck Woody, Seayoung Rhee, and Scott Klein, get a real-world look at the different ways you can efficiently embed predictive analytics in your big data solutions, and explore best practices for analyzing trends and patterns. Find out about extending Azure ML using the Azure ML API services, and look at scenarios and methods for monetizing your ML application with Azure Marketplace.
NOTE: To get the most out of this course, set up the Azure Machine Learning trial beforehand.
Instructor | Seayoung Rhee - Microsoft Senior Technical Product Manager; Buck Woody - Microsoft Senior Technical Specialist; Scott Klein - Microsoft Senior Technical Evangelist
Introduction to Machine Learning & Azure ML Studio
Learn the meaning of Machine Learning and its benefits, and get a quick introduction to basic techniques. See a demo of the Azure Machine Learning portal, and tour the ML Studio.
Designing a Predictive Analytics Solution with Azure ML
Watch an end-to-end scenario demo, and recreate a recommendation model from scratch in ML Studio. Learn about the process and flow of machine learning and what each module contributes, from start to finish.
Monetizing Your ML Application with Azure Marketplace
See a demo on publishing the finished app: begin with the two stage-process of publishing the app and then releasing it to production as a web service. Then, explore the process of registering as a publisher and of submitting the app to the Azure Data Marketplace for approval for monetization.
Azure ML API Services and Extensibility Scenarios
Learn to use the automatically generated C# code in the web service API, and run that code in Visual Studio. This code calls the API from the web service and returns the results, which can be used to embed Machine Learning technologies.
Learn
Explore data mining and predictive analytics.
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Views: 2108
Big Data Training

Welcome to part five of the Deep Learning with Neural Networks and TensorFlow tutorials. Now that we've covered a simple example of an artificial neural network, let's further break this model down and learn how we might approach this if we had some data that wasn't preloaded and setup for us. This is usually the first challenge you will come up against afer you learn based on demos. The demo works, and that's awesome, and then you begin to wonder how you can stuff the data you have into the code. It's always a good idea to grab a dataset from somewhere, and try to do it yourself, as it will give you a better idea of how everything works and what formats you need data in.
Positive data: https://pythonprogramming.net/static/downloads/machine-learning-data/pos.txt
Negative data: https://pythonprogramming.net/static/downloads/machine-learning-data/neg.txt
https://pythonprogramming.net
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex

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sentdex

This tutorial shows how to construct a predictive model using IBM SPSS Modeler. We use the Boston Housing dataset for our illustration. In addition, we also discuss how to evaluate the performance of the model using different nodes such as Graph Evaluation and Data Analysis Node. I hope you enjoy it and please let me know if you have any questions. Thanks for watching.

Views: 14507
IT_CHANNEL

Simple introduction video on how to run neural networks and random forests in weka.

Views: 10277
Gaurav Jetley

Here I will explain Neural networks in R for Machine learning working,how to fit a machine learning model like neural network in R,plotting neural network for machine learning in R,predictions using neural network in R.neuralnet package is used for this modelling.Also I have described the basic Machine learning modelling procedure in R.Its a neural network tutorial for Machine Learning .

Views: 51162
Data Science by Arpan Gupta IIT,Roorkee

Tutorial Rapidminer Data Mining Neural Network (Dataset Training and Scoring)

Views: 4593
Wahyu adi putra

In this video we have explain Back propagation concept used in machine learning
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Last moment tuitions

Intro to machine learning and specifically neural networks. Setting up a neural network using the PHP FANN extension and basic training.

Views: 21072
PHPixie

Support Vector Machines Video (Part 1): http://youtu.be/LXGaYVXkGtg
Support Vector Machine (SVM) Part 2: Non Linear SVM http://youtu.be/6cJoCCn4wuU
Other Videos on Neural Networks:
http://scholastic.teachable.com/p/pattern-classification
Part 2: http://youtu.be/K5HWN5oF4lQ (Multi-layer Perceptrons)
Part 3: http://youtu.be/I2I5ztVfUSE (Backpropagation)
More video Books at: http://scholastictutors.webs.com/
Here we explain how to train a single layer perceptron model using some given parameters and then use the model to classify an unknown input (two class liner classification using Neural Networks)

Views: 135640
homevideotutor

Let's discuss the math behind back-propagation. We'll go over the 3 terms from Calculus you need to understand it (derivatives, partial derivatives, and the chain rule and implement it programmatically.
Code for this video:
https://github.com/llSourcell/how_to_do_math_for_deep_learning
Please Subscribe! And like. And comment. That's what keeps me going.
I've used this code in a previous video. I had to keep the code as simple as possible in order to add on these mathematical explanations and keep it at around 5 minutes.
More Learning resources:
https://mihaiv.wordpress.com/2010/02/08/backpropagation-algorithm/
http://outlace.com/Computational-Graph/
http://briandolhansky.com/blog/2013/9/27/artificial-neural-networks-backpropagation-part-4
https://jeremykun.com/2012/12/09/neural-networks-and-backpropagation/
https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/
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Views: 122586
Siraj Raval

Hey everyone! This is the first in a series of videos teaching you everything you could possibly want to know about neural networks, from the math behind them to how to create one yourself and use it solve your own problems!
This video is meant to be an a quick intro to what neural nets can do, and get us rolling with a simple dataset and problem to solve.
In the next video I'll cover how to use a neural network to automate the task our farmer character solves manually here.

Views: 120286
giant_neural_network

This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into this video to understand how a neural network actually work.
Below topics are explained in this neural network Tutorial:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/Gn1frA
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
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You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
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4. Build deep learning models in TensorFlow and interpret the results
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There is booming demand for skilled deep learning engineers across a wide range of industries, making this deep learning course with TensorFlow training well-suited for professionals at the intermediate to advanced level of experience. We recommend this deep learning online course particularly for the following professionals:
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Views: 1860
Simplilearn

Classification is a predictive modelling. Classification consists of assigning a class label to a set of unclassified cases
Steps of Classification:
1. Model construction: Describing a set of predetermined classes
Each tuple/sample is assumed to belong to a predefined class, as determined by the class label attribute.
The set of tuples used for model construction is training set.
The model is represented as classification rules, decision trees, or mathematical formulae.
2. Model usage: For classifying future or unknown objects
Estimate accuracy of the model
If the accuracy is acceptable, use the model to classify new data
MLP- NN Classification Algorithm
The MLP-NN algorithm performs learning on a multilayer feed-forward neural network. It iteratively learns a set of weights for prediction of the class label of tuples. A multilayer feed-forward neural network consists of an input layer, one or more hidden layers, and an output layer.
Each layer is made up of units. The inputs to the network correspond to the attributes measured for each training tuple. The inputs are fed simultaneously into the units making up the input layer. These inputs pass through the input layer and are then weighted and fed simultaneously to a second layer of “neuronlike” units, known as a hidden layer. The outputs of the hidden layer units can be input to another hidden layer, and so on. The number of hidden layers is arbitrary, although in practice, usually only one is used.
The weighted outputs of the last hidden layer are input to units making up the output layer, which emits the network’s prediction for given tuples.
Algorithm of MLP-NN is as follows:
Step 1: Initialize input of all weights with small random numbers.
Step 2: Calculate the weight sum of the inputs.
Step 3: Calculate activation function of all hidden layer.
Step 4: Output of all layers
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E2MATRIX RESEARCH LAB

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. Check it out!
GitHub repository: https://github.com/sebastianbk/BreastCancerNeuralNetwork

Views: 22295
Sebastian Brandes Kraaijenzank

Training a single neuron with Excel spreadsheet Turner, Scott (2017): Artificial Neural Network - Training a single Neuron using Excel. figshare.
https://doi.org/10.6084/m9.figshare.5339872.v2

Views: 25424
Scott Turner

Tutorial RapidMiner Data Mining Neural Network UNISNU Jepara Fakultas Sains dan Teknologi Program Studi Teknik Informatika

Views: 1544
Suharno Anakdesa

A quick tutorial on analysing data in Orange using Classification.

Views: 34389
haikel5

Provides steps for applying artificial neural networks to do classification and prediction.
R file: https://goo.gl/VDgcXX
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- neural network model
- input, hidden, and output layers
- min-max normalization
- prediction
- confusion matrix
- misclassification error
- network repetitions
- example with binary data
neural network is an important tool related to analyzing big data or working in data science field. Apple has reported using neural networks for face recognition in iPhone X.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 18472
Bharatendra Rai

Source: neuralnet: Training of Neural Network by Frauke Gunther and Stefan Fritsch - The R Journal Vol. 2/1, June 2010

Views: 75221
Hamed Hasheminia

Simplest explanation of Artificial Neural Network in Hindi

Views: 17150
Red Apple Tutorials

In this video I explain how SVM (Support Vector Machine) algorithm works to classify a linearly separable binary data set.
The original presentation is available at http://prezi.com/jdtqiauncqww/?utm_campaign=share&utm_medium=copy&rc=ex0share

Views: 441523
Thales Sehn Körting

23-minute beginner-friendly introduction to data mining with WEKA. Examples of algorithms to get you started with WEKA: logistic regression, decision tree, neural network and support vector machine. Update 7/20/2018: I put data files in .ARFF here http://pastebin.com/Ea55rc3j and in .CSV here http://pastebin.com/4sG90tTu Sorry uploading the data file took so long...it was on an old laptop.

Views: 409120
Brandon Weinberg

Find the notes of ARTIFICIAL NEURAL NETWORKS in this link - https://viden.io/knowledge/artificial-neural-networks-ppt?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1

Views: 37128
LearnEveryone

Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.ac.in

Views: 381319
nptelhrd

Views: 856
InsiderMiner

Data Mining Demo Video on:
- Decision Tree
- Neural Networks

Views: 889
Ayame Shiba

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.
Helpful diagram: https://www.dropbox.com/s/vj0qg9jlmy3mwof/Explanation_1.pdf?dl=0
This particular video goes from the derivative of the sigmoid itself to the delta for the output layer
The presentation can be found here: https://www.dropbox.com/s/z5bz0cw0boxxon1/BackPropagation.pdf?dl=0

Views: 269729
Ryan Harris

This video helps to understand the neural networks modeling in the MATLAB. The nntool is GUI in MATLAB. To use it you dont need any programming knowledge. This tool is very useful for biology people who wants to use ANN for their data.

Views: 58786
sathish thadikamala

For downloadable versions of these lectures, please go to the following link:
http://www.slideshare.net/DerekKane/presentations
This lecture provides an overview of biological based learning in the brain and how to simulate this approach through the use of feed-forward artificial neural networks with back propagation. We will go through some methods of calibration and diagnostics and then apply the technique on three different data mining tasks: binary prediction, classification, and time series prediction.

Views: 11917
Derek Kane

Views: 26339
Markus Hofmann

Use graphical tools to apply neural networks to data fitting, pattern recognition, clustering, and time series problems.
Top 7 Ways to Get Started with Deep Learning and MATLAB: https://goo.gl/1F3adg
Get a Free MATLAB Trial: https://goo.gl/C2Y9A5
Ready to Buy: https://goo.gl/vsIeA5

Views: 261353
MATLAB

We show how to perform artificial neural network prediction using Visual Gene Developer, a free software.
In this tutorial, neural network is trained to learn a complicated function like y = Sin(x) + Abs(y)*Cos(z)
Caption included. Please turn on caption

Views: 77748
Visual Gene Developer

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