Search results “Data mining neural networks example”

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

Views: 3989
prasana sarma

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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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: 64127
Great Learning

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: 36238
Naveen Kumar

back propagation topic in neural networks in simple way to understand. check this link for example https://www.youtube.com/watch?v=0e0z28wAWfg

Views: 45178
Naveen Kumar

Here's a small backpropagation neural network that counts and an example and an explanation for how it works, how it learns. A neural network is a tool in artificial intelligence that learns how to do things instead of be hand-coded. Recently it's been popularized with Google's deep dreaming neural network and Deepmind's AlphaGo beating world champion Lee Sedol at the game of go.
My webpage with all the demo, neural network source code, and so on that I talked about is at:
http://rimstar.org/science_electronics_projects/backpropagation_neural_network_software_3_layer.htm
This video was made possible in part by support from:
Jonathan Rieke
James Padden
Support RimstarOrg on Patreon https://www.patreon.com/user?u=680159
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See also:
Does Volts or Amps Kill You? Voltage, Current and Resistance
https://www.youtube.com/watch?v=9iKD7vuq-rY&list=PLFsZmHTZL-znMsTduihhoYU21XL-d3p3K
How a Rocket Works/Earth to Space Eg SpaceX Falcon 9 and Dragon
https://www.youtube.com/watch?v=L0AMQ6kRNMA&list=PL36D7ABECE2AC9666
How a Crystal Radio Works
http://www.youtube.com/watch?v=0-PParSmwtE&list=PLFsZmHTZL-zlSltC6ELZW9PK4ks7wgPRz
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The Deep dream images were made using the Deep Dream Generator at:
http://deepdreamgenerator.com/
The brain image was extracted from an animated GIF by Doctor Jana http://docjana.com/#/alzheimers
https://creativecommons.org/licenses/by/4.0/deed.en

Views: 167655
RimstarOrg

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%.
Credits:
Nickey Pickorita (YouTube art) -
https://www.upwork.com/freelancers/~0147b8991909b20fca
Isabel Descutner (Voice) -
https://www.youtube.com/user/IsabelDescutner
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https://www.linkedin.com/in/danielpartynski
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Views: 365350
DeepLearning.TV

In this video we have explain Back propagation concept used in machine learning
visit our website for full course
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Ml full notes rupees 200 only
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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: 19529
Last moment tuitions

Views: 864
InsiderMiner

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
Please subscribe! And like. And comment. That's what keeps me going.
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Views: 132541
Siraj Raval

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: 33631
Harsh Gaikwad

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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lecture 9 - neural networks

Views: 6096
CaelusBot

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: 416445
Brandon Weinberg

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

Views: 850
M R Dhandhukia

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: 210
E2MATRIX RESEARCH LAB

Neural Networks Demystified
@stephencwelch
Supporting Code:
https://github.com/stephencwelch/Neural-Networks-Demystified
In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday.
Part 1: Data + Architecture
Part 2: Forward Propagation
Part 3: Gradient Descent
Part 4: Backpropagation
Part 5: Numerical Gradient Checking
Part 6: Training
Part 7: Overfitting, Testing, and Regularization

Views: 381584
Welch Labs

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: 19689
Bharatendra Rai

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
Logistic Regression Theory: https://goo.gl/PbGv1h
Time Series Theory : https://goo.gl/54vaDk
Time ARIMA Model in R : https://goo.gl/UcPNWx
Survival Model : https://goo.gl/nz5kgu
Data Science Career : https://goo.gl/Ca9z6r
Machine Learning : https://goo.gl/giqqmx

Views: 49926
Analytics University

A quick tutorial on analysing data in Orange using Classification.

Views: 36066
haikel5

Week 4 (Neural Networks: Representation) - Applications - Examples and Intuitions II
https://www.coursera.org/learn/machine-learning
Machine Learning Coursera by Andrew Ng Full Playlist: https://www.youtube.com/playlist?list=PL0Smm0jPm9WcCsYvbhPCdizqNKps69W4Z

Views: 5302
Manohar Mukku

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: 266238
MATLAB

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: 679
Dvs Teja

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: 133854
giant_neural_network

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

Views: 10517
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: 53767
Data Science by Arpan Gupta IIT,Roorkee

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: 8300
PyData

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 43960
Noureddin Sadawi

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
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Views: 104588
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: 85063
Augmented Startups

Data Mining Neurol Network Example شرح داتامايننك نيورال نيتورك

Views: 2238
Sudets1

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: 339921
caltech

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: 137675
homevideotutor

Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics.
Deep Learning TV on
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Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency.
Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words.
One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word.
The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector.
Two popular tools:
Word2Vec: https://code.google.com/archive/p/word2vec/
Glove: http://nlp.stanford.edu/projects/glove/
Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse.
Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language.
Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis:
“He turned around a team otherwise known for overall bad temperament”
In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive.
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
Marek Scibior (Prezi creator, Illustrator) -
http://brawuroweprezentacje.pl/
Jagannath Rajagopal (Creator, Producer and Director) -
https://ca.linkedin.com/in/jagannathrajagopal

Views: 39244
DeepLearning.TV

Views: 94785
Victor Lavrenko

We're going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I'll explain why we use recurrent nets for time series data, and why LSTMs boost our network's memory power.
Coding challenge for this video:
https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo
Vishal's winning code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier
Jie's runner up code:
https://github.com/jiexunsee/Simple-Inception-Transfer-Learning
More Learning Resources:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://deeplearning.net/tutorial/lstm.html
https://deeplearning4j.org/lstm.html
https://www.tensorflow.org/tutorials/recurrent
http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/
Please subscribe! And like. And comment. That's what keeps me going.
Join other Wizards in our Slack channel:
http://wizards.herokuapp.com/
And please support me on Patreon:
https://www.patreon.com/user?u=3191693
music in the intro is chambermaid swing by parov stelar
Follow me:
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Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/

Views: 409651
Siraj Raval

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: 38436
LearnEveryone

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: 25617
Colin McDonnell

This tutorial covers the implementation of ANN models (using default algorithm: feed-forward back-propagation) and also discusses NID (Neural Interpretation Diagram) for better understanding of results.
For NID use this:
('https://gist.githubusercontent.com/fawda123/7471137/raw/466c1474d0a505ff044412703516c34f1a4684a5/nnet_plot_update.r') into source_url

Views: 5260
Anirban Chakraborty

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

Views: 4789
Wahyu adi putra

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 36827
Noureddin Sadawi

Provides steps for applying deep learning for developing multilayer perceptron Neural Network for multiclass softmax classification.
R file: https://goo.gl/n5Nyvb
Data: https://goo.gl/MYgpLX
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- installing keras package
- read data
- matrix conversion
- normalize
- data partition
- one hot encoding
- sequential model
- compile model
- fit model
- evaluate model
- prediction
- confusion matrix
Deep learning with neural networks 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: 10571
Bharatendra Rai

In this video I describe how the self organizing maps algorithm works, how the neurons converge in the attribute space to the data. It is important to state that I used a very simple map with only two neurons, and I didn't show the connection between the neurons to simplify the video.

Views: 121869
Thales Sehn Körting

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
2. Data scientists
3. Data analysts
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
For more information about Simplilearn’s courses, visit:
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Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 2467
Simplilearn

What's actually happening to a neural network as it learns?
Next video: https://youtu.be/tIeHLnjs5U8
Training data generation: http://3b1b.co/crowdflower
Find the full playlist at http://3b1b.co/neural-networks
The following video is sort of an appendix to this one. The main goal with the follow-on video is to show the connection between the visual walkthrough here, and the representation of these "nudges" in terms of partial derivatives that you will find when reading about backpropagation in other resources, like Michael Nielsen's book or Chis Olah's blog.
Thanks to everyone supporting on Patreon.
http://3b1b.co/nn3-thanks
http://3b1b.co/support
For more on backpropagation:
http://neuralnetworksanddeeplearning.com/chap2.html
https://github.com/mnielsen/neural-networks-and-deep-learning
http://colah.github.io/posts/2015-08-Backprop/
Music by Vincent Rubinetti:
https://vincerubinetti.bandcamp.com/album/the-music-of-3blue1brown
------------------
3blue1brown is a channel about animating math, in all senses of the word animate. And you know the drill with YouTube, if you want to stay posted on new videos, subscribe, and click the bell to receive notifications (if you're into that).
If you are new to this channel and want to see more, a good place to start is this playlist: http://3b1b.co/recommended
Various social media stuffs:
Website: https://www.3blue1brown.com
Twitter: https://twitter.com/3Blue1Brown
Patreon: https://patreon.com/3blue1brown
Facebook: https://www.facebook.com/3blue1brown
Reddit: https://www.reddit.com/r/3Blue1Brown

Views: 665060
3Blue1Brown

Enroll in the course for free at: https://bigdatauniversity.com/courses/machine-learning-with-python/
Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends.
This free Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.
This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!
Connect with Big Data University:
https://www.facebook.com/bigdatauniversity
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https://www.linkedin.com/groups/4060416/profile
ABOUT THIS COURSE
•This course is free.
•It is self-paced.
•It can be taken at any time.
•It can be audited as many times as you wish.
https://bigdatauniversity.com/courses/machine-learning-with-python/

Views: 49378
Cognitive Class

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