Search results “Neural network and data mining”

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

Views: 4308
prasana sarma

#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

A whiteboard animation on how Neural Networks work

Views: 26094
Predictive Analytics Solutions

In this video we have explain Back propagation concept used in machine learning
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Ml full notes rupees 200 only
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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: 25949
Last moment tuitions

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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: 43138
Harsh Gaikwad

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

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: 378155
DeepLearning.TV

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

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: 7123
CaelusBot

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: 57469
Naveen Kumar

Modeling complex input-outcome relationships; Network architecture: layers and nodes; Neural nets and regression models; Training the network; Avoiding over-fitting
This video was created by Professor Galit Shmueli and has been used as part of blended and online courses on Business Analytics using Data Mining.
It is part of a series of 37 videos, all of which are available on YouTube.
For more information:
http://www.dataminingbook.com
https://www.twitter.com/gshmueli
https://www.facebook.com/dataminingbook
Here is the complete list of the videos:
• Welcome to Business Analytics Using Data Mining (BADM)
• BADM 1.1: Data Mining Applications
• BADM 1.2: Data Mining in a Nutshell
• BADM 1.3: The Holdout Set
• BADM 2.1: Data Visualization
• BADM 2.2: Data Preparation
• BADM 3.1: PCA Part 1
• BADM 3.2: PCA Part 2
• BADM 3.3: Dimension Reduction Approaches
• BADM 4.1: Linear Regression for Descriptive Modeling Part 1
• BADM 4.2 Linear Regression for Descriptive Modeling Part 2
• BADM 4.3 Linear Regression for Prediction Part 1
• BADM 4.4 Linear Regression for Prediction Part 2
• BADM 5.1 Clustering Examples
• BADM 5.2 Hierarchical Clustering Part 1
• BADM 5.3 Hierarchical Clustering Part 2
• BADM 5.4 K-Means Clustering
• BADM 6.1 Classification Goals
• BADM 6.2 Classification Performance Part 1: The Naive Rule
• BADM 6.3 Classification Performance Part 2
• BADM 6.4 Classification Performance Part 3
• BADM 7.1 K-Nearest Neighbors
• BADM 7.2 Naive Bayes
• BADM 8.1 Classification and Regression Trees Part 1
• BADM 8.2 Classification and Regression Trees Part 2
• BADM 8.3 Classification and Regression Trees Part 3
• BADM 9.1 Logistic Regression for Profiling
• BADM 9.2 Logistic Regression for Classification
• BADM 10 Multi-Class Classification
• BADM 11 Ensembles
• BADM 12.1 Association Rules Part 1
• BADM 12.2 Association Rules Part 2
• Neural Networks: Part I
• Neural Networks: Part II
• Discriminant Analysis (Part 1)
• Discriminant Analysis: Statistical Distance (Part 2)
• Discriminant Analysis: Misclassification costs and over-sampling (Part 3)

Views: 679
Galit Shmueli

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:
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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: 144480
Siraj Raval

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2lXhDAx].
This video introduces neural networks.
• Learn about the Perceptron
• NN training and non-linearity
• Delve into deep learning
For the latest Big Data and Business Intelligence video tutorials, please visit
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Views: 196
Packt Video

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

Views: 46886
Naveen Kumar

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: 17380
deeplizard

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

Views: 889
InsiderMiner

For More information Please visit
https://www.appliedaicourse.com

Views: 168550
Applied AI Course

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

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

Views: 999
M R Dhandhukia

What is a neural network, neural network terminology, and setting up a network for time series forecasting
This video supports the textbook Practical Time Series Forecasting.
http://www.forecastingbook.com
http://www.galitshmueli.com

Views: 13003
Galit Shmueli

( 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
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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!
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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.
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Views: 53652
edureka!

So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever.
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Views: 364797
CrashCourse

In this video, we explain the concept of using an artificial neural network to predict on new data. We also show how to predict in code with Keras.
blog: http://deeplizard.com/learn/video/Z0KVRdE_a7Q
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Recommended books:
The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive: http://amzn.to/2GtjKqu

Views: 4644
deeplizard

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

Views: 11066
Gaurav Jetley

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: 109021
sentdex

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

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.
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Logistic Regression in R: https://goo.gl/S7DkRy
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Views: 52460
Analytics University

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

For downloadable versions of these lectures, please go to the following link:
http://www.slideshare.net/DerekKane/presentations
https://github.com/DerekKane/YouTube-Tutorials
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: 12271
Derek Kane

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

Views: 1897
Suharno Anakdesa

Lecture 6 Business Data Mining (Artificial Neural Network and Support Vector Machine)

Views: 84
Phayung Meesad

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

Views: 7368
Chawannut Prommin

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.
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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: 40949
DeepLearning.TV

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: 40288
LearnEveryone

Views: 27364
Markus Hofmann

What is Artificial Neuron Network?
Watch and listen carefully to this video and you will get the idea.
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Views: 1194
benedicta novie

A quick tutorial on analysing data in Orange using Classification.

Views: 38686
haikel5

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

Views: 1038
Ayame Shiba

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2n53Vi6].
Before working on neural networks, we need to understand the theory behind neural networks.
• Understand the logic behind neural networks
• Understand different types of neural networks
For the latest Big Data and Business Intelligence video tutorials, please visit
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Views: 70
Packt Video

Artificial Neural Network - Tugas Kelompok Data Mining 2016
Artificial Neural Network (Jaringan Syaraf Tiruan) ANN adalah sistem komputasi dimana arsitektur dan operasi diilhami dari pengetahuan tentang sel syaraf biologi di dalam otak. Artificial Neural Network (Jaringan Syaraf Tiruan) merupakan model yang meniru cara kerja jaringan neural biologis.

Views: 1073
Aloysius Wiranata

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© 2018 Market leader intermediate business english course book david cotton

The investment world is changing constantly, which means you must update your knowledge continually. Rather than being satisfied with what you already know, keep on learning . Tools and tips. Investment Portfolio Management. Investment Portfolio Management is the art of putting together and managing various investments to meet specific goals. We will examine management strategy choices, asset allocation and investing strategies, and management of risk as they pertain to management of an investment portfolio. Management Strategies. Passive Management. Passive management is for investors willing to accept market returns. Using a fixed asset allocation with a portfolio comprised of index funds would be examples of passive management. Active Management. Asset Allocation Strategies. Strategic Asset Allocation.