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How Artificial Neural Network (ANN) Algorithm Work | Data Mining | Introduction to Neural Network
 
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#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 - 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 Know More about Great Lakes Analytics Programs: PG Program in Business Analytics (PGP-BABI): http://bit.ly/2f4ptdi PG Program in Big Data Analytics (PGP-BDA): http://bit.ly/2eT1Hgo Business Analytics Certificate Program: http://bit.ly/2wX42PD #ANN #MachineLearning #DataMining #NeuralNetwork About Great Learning: - Great Learning is an online and hybrid learning company that offers high-quality, impactful, and industry-relevant programs to working professionals like you. These programs help you master data-driven decision-making regardless of the sector or function you work in and accelerate your career in high growth areas like Data Science, Big Data Analytics, Machine Learning, Artificial Intelligence & more. - 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 - Do you know what the three pillars of Data Science? Here explaining all about the pillars of Data Science: https://www.youtube.com/watch?v=xtI2Qa4v670 - Want to know more about the careers in Data Science & Engineering? Watch this video: https://www.youtube.com/watch?v=0Ue_plL55jU - For more interesting tutorials, don't forget to Subscribe our channel: https://www.youtube.com/user/beaconelearning?sub_confirmation=1 - Learn More at: https://www.greatlearning.in/ For more updates on courses and tips follow us on: - Google Plus: https://plus.google.com/u/0/108438615307549697541 - Facebook: https://www.facebook.com/GreatLearningOfficial/ - LinkedIn: https://www.linkedin.com/company/great-learning/ 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: 71447 Great Learning
Neural Network in Data Mining
 
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Analysis Of Neural Networks in Data Mining by, Venkatraam Balasubramanian Master's in Industrial and Human Factor Engineering
Views: 6004 prasana sarma
Back Propagation in Neural Network with an example
 
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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: 180657 Naveen Kumar
Neural Networks in Data Mining | MLP Multi layer Perceptron Algorithm in Data Mining
 
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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: Website : http://www.e2matrix.com Blog : http://www.e2matrix.com/blog/ WordPress : https://teche2matrix.wordpress.com/ Blogger : https://teche2matrix.blogspot.in/ Contact Us : +91 9041262727 Follow Us on Social Media Facebook : https://www.facebook.com/etwomatrix.researchlab Twitter : https://twitter.com/E2MATRIX1 LinkedIn : https://www.linkedin.com/in/e2matrix-training-research Google Plus : https://plus.google.com/u/0/+E2MatrixJalandhar Pinterest : https://in.pinterest.com/e2matrixresearchlab/ Tumblr : https://www.tumblr.com/blog/e2matrix24
Back Propagation Algorithm / Back Propagation Of Error (Part-1)Explained With Solved Example in Hind
 
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Back Propagation Algorithm Part-2 https://youtu.be/GiyJytfl1Fo 📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 41708 5 Minutes Engineering
Introduction to Neural Networks with Example in HINDI | Artificial Intelligence
 
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Views: 21531 Gate Smashers
How SOM (Self Organizing Maps) algorithm works
 
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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: 157489 Thales Sehn Körting
SSAS - Data Mining - Decision Trees, Clustering, Neural networks
 
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SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1598 M R Dhandhukia
Two Effective Algorithms for Time Series Forecasting
 
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In this talk, Danny Yuan explains intuitively fast Fourier transformation and recurrent neural network. He explores how the concepts play critical roles in time series forecasting. Learn what the tools are, the key concepts associated with them, and why they are useful in time series forecasting. Danny Yuan is a software engineer in Uber. He’s currently working on streaming systems for Uber’s marketplace platform. This video was recorded at QCon.ai 2018: https://bit.ly/2piRtLl For more awesome presentations on innovator and early adopter topics, check InfoQ’s selection of talks from conferences worldwide http://bit.ly/2tm9loz Join a community of over 250 K senior developers by signing up for InfoQ’s weekly Newsletter: https://bit.ly/2wwKVzu
Views: 57263 InfoQ
Neural network and decision tree algorithm
 
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In this video, I have used Neural network and Decision tree algorithms, in SAS EM to predict churn in the telecom industry (from bigml machine learning repository). I have elaborated on 4 data mining concepts, and compared the two models.
Views: 31 Monica Hans
Artificial Neural Network Tutorial | Deep Learning With Neural Networks | Edureka
 
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( 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: 85774 edureka!
Ensemble Learning, Bootstrap Aggregating (Bagging) and Boosting
 
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#EnsembleLearning #EnsembleModels #MachineLearning #DataAnalytics #DataScience Ensemble Learning is using multiple learning algorithms at a time, to obtain predictions with an aim to have better predictions than the individual models. Ensemble learning is a very popular method to improve the accuracy of a machine learning model. It avoid overfitting and gives us a much better model. bootstrap aggregating (Bagging) and boosting are popular ensemble methods. In the next tutorial we will implement some ensemble models in scikit learn. For all Ipython notebooks, used in this series : https://github.com/shreyans29/thesemicolon Facebook : https://www.facebook.com/thesemicolon.code Support us on Patreon : https://www.patreon.com/thesemicolon Check out the machine learning, deep learning and developer products USA: https://www.amazon.com/shop/thesemicolon India: https://www.amazon.in/shop/thesemicolon
Views: 45126 The Semicolon
Neural Networks Explained - Machine Learning Tutorial for Beginners
 
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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: 172086 LearnCode.academy
Back Propagation Algorithm
 
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Back propagation algorithm is used for error detection and correction in Neural Network.
Views: 17324 Rudra Singh
Back Propagation in Machine Learning in Hindi | Machine learning Tutorials
 
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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: 75732 Last moment tuitions
Artificial Neural Networks  (Part 1) -  Classification using Single Layer Perceptron Model
 
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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: 153881 homevideotutor
What is Neural Network in Hindi | How it works | Artificial Intelligence | ProxyNotes
 
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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: 28614 ProxyNotes
Neural network tutorial: The back-propagation algorithm (Part 1)
 
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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: 285796 Ryan Harris
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning ►FREE YOLO GIFT - http://augmentedstartups.info/yolofreegiftsp ►KERAS COURSE - https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML ►MACHINE LEARNING COURSES -http://augmentedstartups.info/machine-learning-courses ------------------------------------------------------------------------ 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. ------------------------------------------------------------ Support us on Patreon ►AugmentedStartups.info/Patreon Chat to us on Discord ►AugmentedStartups.info/discord Interact with us on Facebook ►AugmentedStartups.info/Facebook Check my latest work on Instagram ►AugmentedStartups.info/instagram Learn Advanced Tutorials on Udemy ►AugmentedStartups.info/udemy ------------------------------------------------------------ To learn more on Artificial Intelligence, Augmented Reality IoT, Deep Learning FPGAs, Arduinos, PCB Design and Image Processing then check out http://augmentedstartups.info/home Please Like and Subscribe for more videos :)
Views: 238694 Augmented Startups
Simple Deep Neural Networks for Text Classification
 
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Hi. In this video, we will apply neural networks for text. And let's first remember, what is text? You can think of it as a sequence of characters, words or anything else. And in this video, we will continue to think of text as a sequence of words or tokens. And let's remember how bag of words works. You have every word and forever distinct word that you have in your dataset, you have a feature column. And you actually effectively vectorizing each word with one-hot-encoded vector that is a huge vector of zeros that has only one non-zero value which is in the column corresponding to that particular word. So in this example, we have very, good, and movie, and all of them are vectorized independently. And in this setting, you actually for real world problems, you have like hundreds of thousands of columns. And how do we get to bag of words representation? You can actually see that we can sum up all those values, all those vectors, and we come up with a bag of words vectorization that now corresponds to very, good, movie. And so, it could be good to think about bag of words representation as a sum of sparse one-hot-encoded vectors corresponding to each particular word. Okay, let's move to neural network way. And opposite to the sparse way that we've seen in bag of words, in neural networks, we usually like dense representation. And that means that we can replace each word by a dense vector that is much shorter. It can have 300 values, and now it has any real valued items in those vectors. And an example of such vectors is word2vec embeddings, that are pretrained embeddings that are done in an unsupervised manner. And we will actually dive into details on word2vec in the next two weeks. But, all we have to know right now is that, word2vec vectors have a nice property. Words that have similar context in terms of neighboring words, they tend to have vectors that are collinear, that actually point to roughly the same direction. And that is a very nice property that we will further use. Okay, so, now we can replace each word with a dense vector of 300 real values. What do we do next? How can we come up with a feature descriptor for the whole text? Actually, we can use the same manner as we used for bag of words. We can just dig the sum of those vectors and we have a representation based on word2vec embeddings for the whole text, like very good movie. And, that's some of word2vec vectors actually works in practice. It can give you a great baseline descriptor, a baseline features for your classifier and that can actually work pretty well. Another approach is doing a neural network over these embeddings.
Views: 22368 Machine Learning TV
Lecture 10 - Neural Networks
 
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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: 365888 caltech
Ensemble learners
 
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This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 51003 Udacity
Seminar on Neural Network - Datamining
 
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Presented by Karthik A
Views: 1182 Karthik Gowda
Neural Network Explained -Artificial Intelligence - Hindi
 
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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. Extra Tags neural networks nptel, neural networks in artificial intelligence, neural networks in hindi, neural networks and deep learning, neural networks in r, neural networks in ai, neural networks andrew ng, neural networks in python, neural networks mit, neural networks and fuzzy logic, neural networks, neural networks tutorial, neural networks and deep learning coursera, neural networks applications, neural networks api, neural networks ai, neural networks algorithm, neural networks andrej karpathy, neural networks artificial intelligence, neural networks basics, neural networks brain, neural networks backpropagation, neural networks backpropagation example, neural networks biology, neural networks by rajasekaran free download, neural networks backpropagation tutorial, neural networks blockchain, neural networks basics pdf, neural networks bias, neural networks course, neural networks car, neural networks caltech, neural networks computerphile, neural networks demystified, neural networks demo, neural networks demystified part 1 data and architecture, neural networks data mining, neural networks demystified part 1, neural networks deep learning, neural networks demystified part 3, neural networks demystified part 2, neural networks data analytics, neural networks documentary, neural networks example, neural networks explained, neural networks edureka, neural networks explained simply, neural networks explanation, neural networks evolution, neural networks eli5, neural networks explained simple, neural networks for image recognition, neural networks for dummies, neural networks for recommender systems, neural networks for machine learning youtube, neural networks geoffrey hinton, neural networks game, neural networks google, neural networks gradient, neural networks gradient descent, neural networks genetic algorithms, neural networks gesture recognition, neural networks generations, neural networks graphics, neural networks playing games, neural networks hinton, neural networks hugo larochelle, neural networks harvard, neural networks hardware implementation, neural networks how it works, neural networks handwriting recognition, neural networks human brain, neural networks how they work, neural networks hidden units, neural networks hidden layer, neural networks in data mining, neural networks in machine learning, neural networks introduction, neural networks in tamil, neural networks in c++, neural networks java, neural networks java tutorial, neural networks javascript, neural networks jmp, neural networks js, jeff heaton neural networks, introduction to neural networks for java, neural networks khan academy, neural networks knime, recurrent neural networks keras, neural networks for kids, neural networks lecture, neural networks lecture notes, neural networks learn, neural networks linear regression, neural networks logistic regression, neural networks lstm, neural networks learning algorithms, neural networks lecture videos, neural networks lottery prediction, neural networks loss, neural networks machine learning, neural networks matlab, neural networks matlab tutorial, neural networks mathematics, neural networks music, neural networks mit opencourseware, neural networks math, neural networks meaning in tamil, neural networks mit ocw, neural networks nlp, neural networks nptel videos, neural networks numericals, neural networks ng, neural networks natural language processing, backpropagation in neural networks nptel, andrew ng neural networks, neural networks ocw, neural networks on fpga, neural networks ocr, neural networks perceptron, neural networks python tutorial, neural networks ppt, neural networks ppt download, neural networks questions and answers, neural networks robot, neural networks radiology, neural networks regularization, neural networks recurrent, neural networks rapidminer, neural networks using r, neural networks stanford, neural networks siraj, neural networks spss, neural networks sigmoid function, neural networks simple, neural networks simplified, neural networks sentdex, neural networks siraj raval, neural networks stock market, neural networks simulation, neural networks training, neural networks ted, neural networks tensorflow, neural networks types, neural networks tensorflow tutorial, neural networks tutorial python, neural networks trading, neural networks tutorial youtube,tworks 1, neural networks 2016, neural networks 3blue1brown, neural networks 3d, neural networks 3d reconstruction, neural networks in 4 minutes, lecture 9 - neural networks
Views: 10900 CaelusBot
More Data Mining with Weka (5.1: Simple neural networks)
 
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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: 23543 WekaMOOC
Genetic algorithm in neural network in Hindi with solved example
 
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The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. Genetic algorithm with flow chart diagram and pseudo code with solved example Some topics-- Metrix chain multiplication DAA in hindi https://youtu.be/9LHQRnmW_OE Perceptron learning Algorithm In hindi https://youtu.be/x3joYu5VI38 Neural network in hindi playlist https://www.youtube.com/playlist?list=PLqLEnFoF-ykdiMeuGRCy8yoWpuOdDz-jy Computer graphics in hindi dda algo https://www.youtube.com/playlist?list=PLqLEnFoF-ykeSyrdq7oag5Xfs79nUXSce Python in hindi playlist https://www.youtube.com/playlist?list=PLqLEnFoF-ykfWg6g1JY7ixqO86o1wPzZu Rest api using java https://www.youtube.com/playlist?list=PLqLEnFoF-ykdZf0kcJfWX548yrItxtAnv Web services by piyush sir https://www.youtube.com/watch?v=WihA-ZZ51l8&list=PLqLEnFoF-ykdZf0kcJfWX548yrItxtAnv Automata in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykcMCOVWYzv2EaNjsh7KI-FH Django full playlist in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykcD3-Gkppoq4FReU2q1DMOW Optimal binary search tree https://www.youtube.com/playlist?list=PLqLEnFoF-ykdpNwj0dxZIqNS-7wu2ejid Loops in java https://www.youtube.com/playlist?list=PLqLEnFoF-ykeNQy6UkSh6-S1UBhDoDN6L jsp tutorial in Hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykd4v-yaf4H1f6h6exbwfqMI String handling in java https://www.youtube.com/playlist?list=PLqLEnFoF-ykegkqwCrPcKoe2Akj4_SOGk Spring lecture https://www.youtube.com/playlist?list=PLqLEnFoF-ykfNI2opX4ieZksm1RKce-Mw Jdbc lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykfNI2opX4ieZksm1RKce-Mw Oracle lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykebhrlEZzeSpyDbka_sGAml Android lactures https://www.youtube.com/playlist?list=PLqLEnFoF-ykdHUoBMd1LJZKD7EJiCmX-N Compiler design in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykepOgKaNDiO7JgwXMPHsQLk Automatic review request https://www.youtube.com/watch?v=l2b9DiAIySY&list=PLqLEnFoF-ykcQ4FHNiJ7RPr9otPM5gWmV&index=9 Html tutorials https://www.youtube.com/playlist?list=PLqLEnFoF-ykdEpnKfWUJlCXS_f17sL9Dt Php lactures in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykfN35jmbTJ39qPeOLhXAN4P Operating system in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykezceIJKIEEuPX-2fI31HkJ Mobile computing in hindi https://www.youtube.com/playlist?list=PLqLEnFoF-ykfwbQgfCYVtZxzyQzgfXMOB Hebb learning algorithm https://youtu.be/n4QxeET2hTo auto assosiative algorithm https://youtu.be/2nLp-2z6wG4 genetic algorithm applications genetic algorithm example genetic algorithm tutorial genetic algorithm pdf genetic algorithm python genetic algorithm matlab genetic algorithm steps genetic algorithm machine learning genetic algorithm example genetic algorithm youtube genetic algorithm in artificial intelligence genetic algorithm in soft computing genetic algorithm step by step example max one problem genetic algorithm introduction to genetic algorithm genetic algorithm in artificial intelligence examples genetic algorithm in neural network ppt genetic algorithm neural network python genetic algorithm neural network matlab training feedforward neural networks using genetic algorithms keras genetic algorithm neural network fitness function genetic algorithm neural network github pytorch genetic algorithm genetic algorithm tutorial genetic algorithm example in artificial intelligence genetic algorithm mit genetic algorithm steps genetic algorithm example code in r coding a genetic algorithm genetic algorithm matlab genetic algorithm in business genetic algorithm in artificial algorithm genetic algorithm artificial intelligent
Views: 3619 Muo sigma classes
Data Mining with Weka - Neural Networks and Random Forests
 
06:34
Simple introduction video on how to run neural networks and random forests in weka.
Views: 13438 Gaurav Jetley
Prepare your data for ML  | Text Classification Tutorial Pt. 1 (Coding TensorFlow)
 
04:25
@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: 25350 TensorFlow
genetic algorithm in artificial intelligence | genetic algorithm in hindi | Artificial intelligence
 
11:35
Hey friends welcome to well academy here is the topic genetic algorithm in artificial intelligence in hindi DBMS Gate Lectures Full Course FREE Playlist : https://goo.gl/Z7AAyV Facebook Me : https://goo.gl/2zQDpD Click here to subscribe well Academy https://www.youtube.com/wellacademy1 GATE Lectures by Well Academy Facebook Group https://www.facebook.com/groups/1392049960910003/ Thank you for watching share with your friends Follow on : Facebook page : https://www.facebook.com/wellacademy/ Instagram page : https://instagram.com/well_academy Twitter : https://twitter.com/well_academy genetic algorithm in artificial intelligence, genetic algorithm in artificial intelligence in hindi, genetic algorithm in artificial intelligence example, genetic algorithm in artificial intelligence tutorial, genetic algorithm in artificial intelligence in urdu, genetic algorithm in artificial intelligence hindi, genetic algorithm in hindi, genetic algorithm in ai, genetic algorithm artificial intelligence, genetic algorithm, genetic algorithm ai, genetic algorithm well academy, genetic algorithm crossover genetic algorithm tutorial genetic algorithm example genetic algorithm genetic algorithm fitness function genetic algorithm artificial intelligence artificial intelligence well academy well academy artificial intelligence artificial intelligence tutorial artificial intelligence in hindi artificial intelligence lecture artificial intelligence lecture in hindi
Views: 167874 Well Academy
Gradient descent algorithm (neural networks) explanation with derivation in Hindi
 
09:27
The video explains gradient descent algorithm used in machine learning, deep learning with derivation in Hindi. If you need explanation of any other deep learning concept, please write in the comments or mail me @ [email protected] Please like, share and subscribe.
Views: 15200 Harsh Gupta
Top 5 Algorithms used in Data Science | Data Science Tutorial | Data Mining Tutorial | Edureka
 
01:13:27
( Data Science Training - https://www.edureka.co/data-science ) This tutorial will give you an overview of the most common algorithms that are used in Data Science. Here, you will learn what activities Data Scientists do and you will learn how they use algorithms like Decision Tree, Random Forest, Association Rule Mining, Linear Regression and K-Means Clustering. To learn more about Data Science click here: http://goo.gl/9HsPlv The topics related to 'R', Machine learning and Hadoop and various other algorithms have been extensively covered in our course “Data Science”. For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free). Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 109742 edureka!
Decision Tree & Neural Networks - SAS Enterprise Miner
 
07:51
Data Mining Demo Video on: - Decision Tree - Neural Networks
Views: 1541 Ayame Shiba
Top 10 Machine Learning Algorithms to Become a Data Scientist | Data Science Tutorial | Acadgild
 
03:26
Top 10 Machine Learning Algorithms to Become a Data Scientist https://acadgild.com/big-data/data-science-training-certification?aff_id=6003&source=youtube&account=HZEV70u3740&campaign=youtube_channel&utm_source=youtube&utm_medium=ml-algorithms&utm_campaign=youtube_channel Hello and Welcome to Data Science tutorial powered by Acadgild. In this video, you will be able to learn the top 10 machine learning algorithms to learn to become a data scientist. In this tutorial, we will be looking at more algorithm side of it with relation to the particular task. The can be about classification, regression, and clustering. Clustering is part of your unsupervised learning, regression and classification is the part of supervised learning. Classification Algorithms help us to differentiate one group from rest of the observations and there are various algorithms. Classification Algorithms will have: • Support Vector Machines • Discriminant Analysis • Naïve Bayes • Nearest Neighbour • Neural Networks Regression algorithms will have: • Linear Regression, GLM • SVR, GPR • Decision Trees • Ensemble Methods • Neural Networks Clustering Algorithms will have: • K-Means, K-Medoids Fuzzy C-Means • Hierarchical Model • Gaussian Mixture • Hidden Markov Model • Neural Networks Go through the entire video to learn more about Mean median mode. And stay tuned for next session. For more updates on courses and tips follow us on: Facebook: https://www.facebook.com/acadgild Twitter: https://twitter.com/acadgild LinkedIn: https://www.linkedin.com/company/acadgild
Views: 1380 ACADGILD
How CNN (Convolutional Neural Networks - Deep Learning) algorithm works
 
08:56
In this video I present a simple example of a CNN (Convolutional Neural Network) applied to image classification of digits. CNN is one of the well known Deep Learning algorithms. I firstly explain the basics of Neural Networks, i.e. the artificial neuron, followed by the concept of convolution, and the common layers in a CNN, such as convolutional, pooling, fully connected, and softmax classification. I read several references to prepare this material, but the main references are: * Towards better exploiting convolutional neural networks for Remote Sensing scene classification. By Keiller Nogueira, Otávio Penatti, Jefersson dos Santos * Everything you wanted to know about Deep Learning for computer vision but were afraid to ask. By Moacir Ponti, Leonardo Ribeiro, Tiago Nazaré, Tu Bui, John Collomosse I also created an Octave (Matlab like) source code to implement the basic CNN showed in this video, which are available at my github. Please follow the link for more details on the source code: https://github.com/tkorting/youtube/tree/master/deep-learning-cnn This presentation is available at my Prezi site, at this link: https://prezi.com/n_r8p1ytanyh/?utm_campaign=share&utm_medium=copy Thanks for watching this video, please like and share, and subscribe to my channel. Regards
Views: 52205 Thales Sehn Körting
YOW! Data 2018 - Shujia Zhang - Graph Neural Networks: Algorithm and Applications #YOWData
 
21:12
Artificial neural networks help us cluster and classify. Since "Deep learning" became the buzzword, it has been applied for many advances of AI, such as self-driving car, image classification, Alpha Go, etc. There are lots of different deep learning architectures, the most popular ones are based on the well known convolutional neural network which is one type of feed-forward neural networks. This talk will introduce another variant of deep neural network - Graph Neural network which can model the data represented as generic graphs (a graph can have labelled nodes connected via weighted edges). The talk will cover: 1. the graph (graph of graphs - GoGs) representation: how we represent different data with graphs 2. architecture of graph neural networks (GNN): the architecture of deep graph neural networks and learning algorithm 3. applications of GoGs and GNNs: document classification, web spam detection, human action recognition in video Accomplished data science specialist with 10 years hands-on experience on data projects. Has been successful in developing machine learning approaches, which have proven advantage in various problem domains such as data mining, document categorisation, image & video recognition. High degree of expertise in deep artificial neural networks and graph modelling. Currently a data scientist working at SafetyCulture, leading development of innovative AI driven product features. For more on YOW! Conference, visit http://www.yowconference.com.au
Views: 3655 YOW! Conferences
Convolutional Neural Network (CNN) | Convolutional Neural Networks With TensorFlow | Edureka
 
22:14
( 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: 111807 edureka!
Lec-19 Back Propagation Algorithm
 
55:35
Lecture Series on Neural Networks and Applications by Prof.S. Sengupta, Department of Electronics and Electrical Communication Engineering, IIT Kharagpur. For more details on NPTEL visit http://nptel.iitm.ac.in
Views: 70656 nptelhrd
Data Science - Part VIII -  Artifical Neural Network
 
50:04
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: 12992 Derek Kane
Basics Of Principal Component Analysis Explained in Hindi ll Machine Learning Course
 
09:07
📚📚📚📚📚📚📚📚 GOOD NEWS FOR COMPUTER ENGINEERS INTRODUCING 5 MINUTES ENGINEERING 🎓🎓🎓🎓🎓🎓🎓🎓 SUBJECT :- Discrete Mathematics (DM) Theory Of Computation (TOC) Artificial Intelligence(AI) Database Management System(DBMS) Software Modeling and Designing(SMD) Software Engineering and Project Planning(SEPM) Data mining and Warehouse(DMW) Data analytics(DA) Mobile Communication(MC) Computer networks(CN) High performance Computing(HPC) Operating system System programming (SPOS) Web technology(WT) Internet of things(IOT) Design and analysis of algorithm(DAA) 💡💡💡💡💡💡💡💡 EACH AND EVERY TOPIC OF EACH AND EVERY SUBJECT (MENTIONED ABOVE) IN COMPUTER ENGINEERING LIFE IS EXPLAINED IN JUST 5 MINUTES. 💡💡💡💡💡💡💡💡 THE EASIEST EXPLANATION EVER ON EVERY ENGINEERING SUBJECT IN JUST 5 MINUTES. 🙏🙏🙏🙏🙏🙏🙏🙏 YOU JUST NEED TO DO 3 MAGICAL THINGS LIKE SHARE & SUBSCRIBE TO MY YOUTUBE CHANNEL 5 MINUTES ENGINEERING 📚📚📚📚📚📚📚📚
Views: 39219 5 Minutes Engineering
Recurrent Neural Networks (RNN) | RNN LSTM | Deep Learning Tutorial | Tensorflow Tutorial | Edureka
 
31:35
( 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: 80416 edureka!
What is a Neural Network? | How Deep Neural Networks Work | Neural Network Tutorial | Simplilearn
 
13:06
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: 14189 Simplilearn
Boosting
 
02:25
This video is part of the Udacity course "Machine Learning for Trading". Watch the full course at https://www.udacity.com/course/ud501
Views: 133751 Udacity
Loading in your own data - Deep Learning basics with Python, TensorFlow and Keras p.2
 
18:51
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/ Twitter: https://twitter.com/sentdex Facebook: https://www.facebook.com/pythonprogramming.net/ Twitch: https://www.twitch.tv/sentdex G+: https://plus.google.com/+sentdex
Views: 154692 sentdex
K mean clustering algorithm with solve example
 
12:13
#kmean datawarehouse #datamining #lastmomenttuitions Take the Full Course of Datawarehouse What we Provide 1)22 Videos (Index is given down) + Update will be Coming Before final exams 2)Hand made Notes with problems for your to practice 3)Strategy to Score Good Marks in DWM To buy the course click here: https://lastmomenttuitions.com/course/data-warehouse/ Buy the Notes https://lastmomenttuitions.com/course/data-warehouse-and-data-mining-notes/ if you have any query email us at [email protected] Index Introduction to Datawarehouse Meta data in 5 mins Datamart in datawarehouse Architecture of datawarehouse how to draw star schema slowflake schema and fact constelation what is Olap operation OLAP vs OLTP decision tree with solved example K mean clustering algorithm Introduction to data mining and architecture Naive bayes classifier Apriori Algorithm Agglomerative clustering algorithmn KDD in data mining ETL process FP TREE Algorithm Decision tree
Views: 473456 Last moment tuitions
What is backpropagation really doing? | Deep learning, chapter 3
 
13:54
What's actually happening to a neural network as it learns? Next video: https://youtu.be/tIeHLnjs5U8 Brought to you by you: http://3b1b.co/nn3-thanks And by CrowdFlower: http://3b1b.co/crowdflower Home page: https://www.3blue1brown.com/ 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.
Views: 1255572 3Blue1Brown
Machine Learning - Supervised VS Unsupervised Learning
 
05:04
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 https://twitter.com/bigdatau 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: 101878 Cognitive Class