Search results “Data mining neural networks example”

#ArtificialNeuralNetwork | 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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#ANN #MachineLearning #DataMining #NeuralNetwork
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Views: 65418
Great Learning

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

Views: 4308
prasana sarma

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: 57719
Naveen Kumar

A whiteboard animation on how Neural Networks work

Views: 26094
Predictive Analytics Solutions

Contact me on : [email protected]
Neural Networks is one of the most interesting topics in the Machine Learning community. Their potential is being recognized every day as the technology is advancing at an ever growing rate. From being a topic of research for decades to practical use by thousands of organizations, Neural Networks have come a long way. Today there are a number of jobs available in Machine Learning from application to research domain. But Machine Learning is not like conventional programming. It requires a different line of thinking than what conventional programming has taught us.
This might become a problem for people interested in learning Machine Learning. A lot of mathematical concepts are deeply embedded in ML and an understanding of these core concepts will help anyone starting with ML go long way ahead. Trust me! thats the only way.
In this video I have tried to make those core concepts a little bit clearer by using a real-life example. This video is about how simply you can understand the working of an Artificial Neural Network. There are a lot of questions which can come to your mind after watching this video, but do not focus on the "WHY" as much as on the "HOW" of what has been explained.
A detailed explanation of each of the mentioned terms will be covered in the future videos.

Views: 43214
Harsh Gaikwad

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

Views: 1897
Suharno Anakdesa

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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Views: 294
E2MATRIX RESEARCH LAB

In this video, we explain the concept of the different data sets used for training and testing an artificial neural network, including the training set, testing set, and validation set. We also show how to create and specify these data sets in code with Keras.
Check out the corresponding blog and other resources for this video at:
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Recommended books:
The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive: http://amzn.to/2GtjKqu

Views: 17387
deeplizard

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: 9811
PyData

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: 21450
WekaMOOC

ขอชี้แจงเรื่อง input นิดนึง ในคลิป [0,1]
แต่ อ. สอน [-1 1 ]
ความจริงได้ทั้งสองแบบ แต่ยึดตาม อ.สอนก็ได้ -1 1

Views: 7368
Chawannut Prommin

Provides steps for applying artificial neural networks to do classification and prediction.
R file: https://goo.gl/VDgcXX
Data file: https://goo.gl/D2Asm7
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: 21399
Bharatendra Rai

In this video we have explain Back propagation concept used in machine learning
visit our website for full course
www.lastmomenttuitions.com
Ml full notes rupees 200 only
ML notes form : https://goo.gl/forms/7rk8716Tfto6MXIh1
Machine learning introduction : https://goo.gl/wGvnLg
Machine learning #2 : https://goo.gl/ZFhAHd
Machine learning #3 : https://goo.gl/rZ4v1f
Linear Regression in Machine Learning : https://goo.gl/7fDLbA
Logistic regression in Machine learning #4.2 : https://goo.gl/Ga4JDM
decision tree : https://goo.gl/Gdmbsa
K mean clustering algorithm : https://goo.gl/zNLnW5
Agglomerative clustering algorithmn : https://goo.gl/9Lcaa8
Apriori Algorithm : https://goo.gl/hGw3bY
Naive bayes classifier : https://goo.gl/JKa8o2

Views: 26003
Last moment tuitions

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: 28783
Scott Turner

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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Views: 7123
CaelusBot

The main concept of this Data Mining project is to forecast the Closing prices of the stock market based on the past data sets.
Note: Watch with Sub-titles :)

Views: 900
Dvs Teja

( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka "Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you to understand the basics of Neural Networks and how to use it for deep learning. It explains Single layer and Multi layer Perceptron in detail.
Below are the topics covered in this tutorial:
1. Why Neural Networks?
2. Motivation Behind Neural Networks
3. What is Neural Network?
4. Single Layer Percpetron
5. Multi Layer Perceptron
6. Use-Case
7. Applications of Neural Networks
Subscribe to our channel to get video updates. Hit the subscribe button above.
Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
- - - - - - - - - - - - - -
How it Works?
1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - -
About the Course
Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
- - - - - - - - - - - - - -
Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
- - - - - - - - - - - - - -
Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
Please write back to us at [email protected] or call us at +91 88808 62004 for more information.
Facebook: https://www.facebook.com/edurekaIN/
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LinkedIn: https://www.linkedin.com/company/edureka

Views: 53692
edureka!

Views: 98658
Victor Lavrenko

madaline neural network with XOR implementation

Views: 322
btech tutorial

Data Mining Neurol Network Example شرح داتامايننك نيورال نيتورك

Views: 2309
Sudets1

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!
Deep Learning TV on
Facebook: https://www.facebook.com/DeepLearningTV/
Twitter: https://twitter.com/deeplearningtv
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%.
Credits:
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
Dan Partynski (Copy Editing) -
https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Views: 378155
DeepLearning.TV

A quick tutorial on analysing data in Orange using Classification.

Views: 38686
haikel5

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: 58043
Data Science by Arpan Gupta IIT,Roorkee

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

Views: 11066
Gaurav Jetley

In this video I present a simple example of a CNN (Convolutional Neural Network) applied to image classification of digits. CNN is one of the well known Deep Learning algorithms.
I firstly explain the basics of Neural Networks, i.e. the artificial neuron, followed by the concept of convolution, and the common layers in a CNN, such as convolutional, pooling, fully connected, and softmax classification.
I read several references to prepare this material, but the main references are:
* Towards better exploiting convolutional neural networks for Remote Sensing scene classification. By Keiller Nogueira, Otávio Penatti, Jefersson dos Santos
* Everything you wanted to know about Deep Learning for computer vision but were afraid to ask. By Moacir Ponti, Leonardo Ribeiro, Tiago Nazaré, Tu Bui, John Collomosse
I also created an Octave (Matlab like) source code to implement the basic CNN showed in this video, which are available at my github. Please follow the link for more details on the source code:
https://github.com/tkorting/youtube/tree/master/deep-learning-cnn
This presentation is available at my Prezi site, at this link:
http://prezi.com/n_r8p1ytanyh/?utm_campaign=share&utm_medium=copy
Thanks for watching this video, please like and share, and subscribe to my channel.
Regards

Views: 20480
Thales Sehn Körting

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

Views: 5118
Wahyu adi putra

Step by step explanation of how a single layer perceptron artificial neural network (ANN) got trained and tested using an example of RGB colors consisting of two classes (Red and Blue) with two samples per class.
Weights modification is applied when the predicted class is different from the desired class.
After training the network successfully, a new unlabeled sample got applied to the network to predict its class label.
Neural Network Tutorial in Arabic.
.شرح خطوة بخطوة بالعربي لكيفية تدريب وإختبار الشبكات العصبية الإصطناعية
بعد الإنتهاء من تدريب الشبكة, نأتي للمرحلة التي من خلالها نتوقع تصنيف أحد العينات الغير معروف تصنيفها.
Presentation used in the video is available in my SlideShare account:
https://www.slideshare.net/AhmedGadFCIT/introduction-to-artificial-neural-network-stepbystep-training-testing-example
لاى سؤال او لمزيد من المعلومات, قم بترك تعليقا او تواصل معي.
أحمد فوزي جاد Ahmed Fawzy Gad
قسم تكنولوجيا المعلومات Information Technology (IT) Department
كلية الحاسبات والمعلومات Faculty of Computers and Information (FCI)
جامعة المنوفية, مصر Menoufia University, Egypt
Teaching Assistant/Demonstrator
[email protected]
Find me on:
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Views: 30412
Ahmed Gad

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

Views: 109021
sentdex

Support Vector Machine (SVM) - Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples.
To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes.
So how do we decide where to draw our decision boundary?
Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class.
These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors.
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Views: 108079
Augmented Startups

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

Views: 1004
M R Dhandhukia

For cool updates on AI research, follow me at https://twitter.com/iamvriad.
Lecture from the course Neural Networks for Machine Learning, as taught by Geoffrey Hinton (University of Toronto) on Coursera in 2012.
Link to the course (login required): https://class.coursera.org/neuralnets-2012-001

Views: 26682
Colin McDonnell

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: 140684
homevideotutor

This Neural Network tutorial will help you understand what is deep learning, what is a neural network, how deep neural network works, advantages of neural network, applications of neural network and the future of neural network. 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. Deep Learning forms the basis for most of the incredible advances in Machine Learning. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. Now, let us deep dive into this video to understand how a neural network actually works along with some real-life examples.
Below topics are explained in this neural network Tutorial:
1. What is Deep Learning?
2. What is an artificial network?
3. How does neural network work?
4. Advantages of neural network
5. Applications of neural network
6. Future of neural network
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/Hk7cJ1
Watch more videos on Deep Learning: https://www.youtube.com/playlist?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.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
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:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning and artificial intelligence
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:
1. Software engineers
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4. Statisticians with an interest in deep learning
Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-a-nEURAL-nETWORK-VB1ZLvgHlYs&utm_medium=Tutorials&utm_source=youtube
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Views: 5599
Simplilearn

In this video you will learn what are the differences between Supervised Learning & Unsupervised learning in the context of Machine Learning. Linear regression, Logistic regression, SVM, random forest are the supervised learning algorithms.
For all videos and Study packs visit : http://analyticuniversity.com/
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Logistic Regression in R: https://goo.gl/S7DkRy
Logistic Regression in SAS: https://goo.gl/S7DkRy
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Analytics University

Neural Networks - A biologically inspired model. The efficient backpropagation learning algorithm. Hidden layers. Lecture 10 of 18 of Caltech's Machine Learning Course - CS 156 by Professor Yaser Abu-Mostafa. View course materials in iTunes U Course App - https://itunes.apple.com/us/course/machine-learning/id515364596 and on the course website - http://work.caltech.edu/telecourse.html
Produced in association with Caltech Academic Media Technologies under the Attribution-NonCommercial-NoDerivs Creative Commons License (CC BY-NC-ND). To learn more about this license, http://creativecommons.org/licenses/by-nc-nd/3.0/
This lecture was recorded on May 3, 2012, in Hameetman Auditorium at Caltech, Pasadena, CA, USA.

Views: 345122
caltech

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: 274602
MATLAB

► ► Subscribe To My New Artificial Intelligence Newsletter! https://goo.gl/qz1xeZ
Learn how to create a neural network to classify wine in 15 lines of Python with Keras.
Code: https://github.com/jg-fisher/wineNeuralNetwork
Dataset: https://archive.ics.uci.edu/ml/datasets/wine
Keras: https://keras.io/
-- Highly recommended for theoretical and applied ML --
Deep Learning: https://amzn.to/2LomU4y
Hands on Machine Learning: https://amzn.to/2JSxhIv
Hope you guys enjoyed this video! Be sure to leave any comments or questions below, subscribe and thumbs up (:

Views: 2705
John G. Fisher

In the last 5 years we observe rapidly increasing interest to artificial neural networks. But what are neural networks and how do they differ from conventional algorithms?
What kind of problems we can resolve with neural networks that could not solve with traditional approaches? In this session we will answer those questions explaining neural networks principles. We will explain concepts behind neural networks and give insight of neural networks usage such as self-driving cars, image recognition, automated translation and text analysis. We will walk you through real life application example: recognition of hand written digits using classical MNIST dataset and convolutional neural network. As a framework Microsoft CNTK and Tensorflow will be used in practical part of presentation.
NDC Conferences
https://ndc-london.com
https://ndcconferences.com

Views: 864
NDC Conferences

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: 429124
Brandon Weinberg

#ArtificialNeuralNetwork | This machine learning tutorial helps you build an Artificial Neural Network model using R with the help of a hands-on example. You will be understanding the application of Neural Networks in AI and solving business problems.
Visit https://greatlearningforlife.com our learning portal for 100s of hours of similar free high-quality tutorial videos on Python, R, Machine Learning, AI and other similar topics
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- Watch the video to know ''Why is there so much hype around 'Artificial Intelligence'?'' https://www.youtube.com/watch?v=VcxpBYAAnGM
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For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1
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Views: 88101
Great Learning

Provides steps for applying Image classification & recognition using CNN with easy to follow example. CNN is considered 'gold standard' for large scale image classification.
R file: https://goo.gl/trgsuH
Data: https://goo.gl/JmEjmc
Machine Learning videos: https://goo.gl/WHHqWP
Uses TensorFlow (by Google) as backend for CNN and includes,
- Advantages
- layers
- parameter calculations
- load keras and EBImage packages
- read images
- explore images and image data
- resize and reshape images
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
large scale Image Classification & Recognition using cnn with Keras is an important tool related to analyzing big data or working in data science field.
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: 7135
Bharatendra Rai

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: 275117
Ryan Harris

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