#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.
- Watch our new free Python for Data Science Beginners tutorial: https://greatlearningforlife.com/python
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#ANN #MachineLearning #DataMining #NeuralNetwork
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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
- What is Machine Learning & its Applications? https://www.youtube.com/watch?v=NsoHx0AJs-U
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Great Learning has collaborated with the University of Texas at Austin for the PG Program in Artificial Intelligence and Machine Learning and with UT Austin McCombs School of Business for the PG Program in Analytics and Business Intelligence.

Views: 70426
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: 160072
Naveen Kumar

If you know nothing about how a neural network works, this is the video for you! I've worked for weeks to find ways to explain this in a way that is easy to understand for beginners.
Past Videos:
Intro to Machine Learning with Javascript:
https://www.youtube.com/watch?v=9Hz3P1VgLz4&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=2&t=0s
Machine Learning 2 - Building a Recommendation Engine:
https://www.youtube.com/watch?v=lvzekeBQsSo&list=PLoYCgNOIyGABWLy_XoLSxTVRe2bltV8GM&index=3&t=0s
Machine learning and neural networks are awesome. This video provides beginners with an easy tutorial explaining how a neural network works - what math is involved, and a step by step explanation of how the data moves through the network.
The example used will be a feed forward neural network with back propagation. It explains the difference between linear and non linear data, the importance of the activation function, learning rate, and momentum configurations.
-~-~~-~~~-~~-~-
Also watch: "Responsive Design Tutorial - Tips for making web sites look great on any device"
https://www.youtube.com/watch?v=fgOO9YUFlGI
-~-~~-~~~-~~-~-

Views: 141208
LearnCode.academy

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

Views: 5784
prasana sarma

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

Views: 23234
WekaMOOC

( 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.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 78555
edureka!

A whiteboard animation on how Neural Networks work

Views: 29383
Predictive Analytics Solutions

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
For more information and query visit our website:
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Tumblr : https://www.tumblr.com/blog/e2matrix24

Views: 798
E2MATRIX RESEARCH LAB

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: 151410
homevideotutor

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
http://bit.ly/1HCjJik
Find us on Facebook -- http://www.facebook.com/Packtvideo
Follow us on Twitter - http://www.twitter.com/packtvideo

Views: 96
Packt Video

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

Views: 100
Phayung Meesad

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

Views: 2850
Suharno Anakdesa

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: 29498
Bharatendra Rai

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

Views: 84861
Hamed Hasheminia

This video shows what neural network is and how it works in the simplest way possible. As this is a complex concept, we have tried our best to simplify it as much as possible in a limited duration video. We take the help of a child as example and try to understand the complex neural network with the help of this child. This video also demonstrated how neural network works by taking an example of Image Recognition. It shows how values are calculated at each step, how the output is generated and how using back propagation the neural net adjusts its weights and values.
Hope this video helps!
Like our Facebook page: https://www.facebook.com/proxynotes/
Subscribe to our channel on Youtube: https://www.youtube.com/c/ProxyNotes?sub_confirmation=1
- By ProyNotes
#ProxyNotesCS

Views: 23684
ProxyNotes

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

Views: 6484
Wahyu adi putra

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:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simp...
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 12701
Simplilearn

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

Views: 82601
Visual Gene Developer

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: 1948
Dvs Teja

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: 284133
Ryan Harris

Simplest explanation of Artificial Neural Network in Hindi

Views: 47728
Red Apple Tutorials

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: 470523
Brandon Weinberg

Views: 964
InsiderMiner

This Neural Network tutorial will help you understand what is a neural network, how a neural network works, what can the neural network do, types of neural network and a usecase implementation on how to classify between photos of dogs and cats. Deep Learning uses advanced computing power and special types of neural networks and applies them to large amounts of data to learn, understand, and identify complicated patterns. Automatic language translation and medical diagnoses are examples of deep learning. Most deep learning methods involve artificial neural networks, modeling how our brains work. Neural networks are built on Machine Learning algorithms to create an advanced computation model that works much like the human brain. This neural network tutorial is designed for beginners to provide them the basics of deep learning. Now, let us deep dive into this video to understand how a neural network actually work.
Below topics are explained in this neural network Tutorial:
1. What is Neural Network?
2. What can Neural Network do?
3. How does Neural Network work?
4. Types of Neural Network
5. Use case - To classify between the photos of dogs and cats
To learn more about Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1
You can also go through the slides here: https://goo.gl/Gn1frA
Watch more videos on Deep Learning: https://www.youtube.com/watch?v=FbxTVRfQFuI&list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip
#DeepLearning #Datasciencecourse #DataScience #SimplilearnMachineLearning #DeepLearningCourse
Simplilearn’s Deep Learning course will transform you into an expert in deep learning techniques using TensorFlow, the open-source software library designed to conduct machine learning & deep neural network research. With our deep learning course, you'll master deep learning and TensorFlow concepts, learn to implement algorithms, build artificial neural networks and traverse layers of data abstraction to understand the power of data and prepare you for your new role as deep learning scientist.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
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=Neural-Network-Tutorial-ysVOhBGykxs&utm_medium=Tutorials&utm_source=youtube
For more information about Simplilearn’s courses, visit:
- Facebook: https://www.facebook.com/Simplilearn
- Twitter: https://twitter.com/simplilearn
- LinkedIn: https://www.linkedin.com/company/simplilearn/
- Website: https://www.simplilearn.com
Get the Android app: http://bit.ly/1WlVo4u
Get the iOS app: http://apple.co/1HIO5J0

Views: 17078
Simplilearn

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: 12852
Derek Kane

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: 67736
Last moment tuitions

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

Views: 13022
Gaurav Jetley

Complete tutorial on http://www.techjatt.tk/2016/01/iris-flower-data-set-in-matlab-tutorial.html

Views: 45269
Tech Jatt

( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow )
This video will provide you with a brief and crisp knowledge of Neural Networks, how they work, the various parameters involved in the whole Deep Learning Process.
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.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 10020
edureka!

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

Views: 1460
M R Dhandhukia

A quick tutorial on analysing data in Orange using Classification.

Views: 47617
haikel5

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: 206619
giant_neural_network

@lmoroney is back with another episode of Coding TensorFlow! In this episode, we discuss Text Classification, which assigns categories to text documents. This is part 1 of a 2 part sub series that focuses on the data and gets it ready to train a neural network. Laurence also explains the unique challenges associated with Text Classification. Watch to follow along and stay tuned for part 2 of this episode where we’ll look at how to design a neural network to accept the data we prepared.
Hands on tutorial → http://bit.ly/2CNVMbi
Watch Part 2 https://www.youtube.com/watch?v=vPrSca-YjFg
Subscribe to TensorFlow → http://bit.ly/TensorFlow1
Watch more Coding TensorFlow → http://bit.ly/2zoZfvt

Views: 21157
TensorFlow

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 .
#neuralnetwork #machinelearning #datascience #R

Views: 66191
Data Science by Arpan Gupta IIT,Roorkee

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Views: 32316
5 Minutes Engineering

( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka "Convolutional Neural Network Tutorial" video (Blog: https://goo.gl/4zxMfU) will help you in understanding what is Convolutional Neural Network and how it works. It also includes a use-case, in which we will be creating a classifier using TensorFlow.
Below are the topics covered in this tutorial:
1. How a Computer Reads an Image?
2. Why can't we use Fully Connected Networks for Image Recognition?
3. What is Convolutional Neural Network?
4. How Convolutional Neural Networks Work?
5. Use-Case (dog and cat classifier)
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.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 97623
edureka!

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: 122111
sentdex

Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges!
First, we need a dataset. Let's grab the Dogs vs Cats dataset from Microsoft: https://www.microsoft.com/en-us/download/confirmation.aspx?id=54765
Text tutorials and sample code: https://pythonprogramming.net/loading-custom-data-deep-learning-python-tensorflow-keras/
Discord: https://discord.gg/sentdex
Support the content: https://pythonprogramming.net/support-donate/
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Facebook: https://www.facebook.com/pythonprogramming.net/
Twitch: https://www.twitch.tv/sentdex
G+: https://plus.google.com/+sentdex

Views: 118343
sentdex

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

Views: 66632
Hvass Laboratories

For a complete course on machine learning do visit
https://www.udemy.com/demystifying-ma...
For a limited time, it is free

Views: 3896
Data Science Mastery

In this video you will learn Aritificial Neural Network ANN in Artificial Intelligence & Artificial neural network example It is one of the most important topic in Artificial intelligence and what are neural networks used for in Artfiicial intelligence along with neural network and gate.
Furthur more we will be discussing artificial neural network in hindi, artificial neural network in urdu, artificial neural network definition, artificial neural network ppt, artificial neural network pdf, also what is neural network in artificial intelligence, neural system in artificial intelligence, how to learn neural networks, artificial intelligence and neural networks.
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Neural Network Tutorial
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Views: 58628
Malik Shahzaib Official

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

( TensorFlow Training - https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka Recurrent Neural Networks tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks - Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
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.
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka

Views: 72511
edureka!

Provides steps for applying deep neural networks for numeric response or independent variable.
R file: https://goo.gl/MwBLVt
Machine Learning videos: https://goo.gl/WHHqWP
Includes,
- matrix conversion
- normalize
- data partition
- sequential model
- compile model
- fit model
- evaluate model
- prediction
Deep learning with neural networks 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: 6811
Bharatendra Rai