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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: 70426 Great Learning
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: 160072 Naveen Kumar
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: 141208 LearnCode.academy
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: 5784 prasana sarma
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: 23234 WekaMOOC
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: 78555 edureka!
Neural Network in Two and Half Minutes
 
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A whiteboard animation on how Neural Networks work
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
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: 151410 homevideotutor
Advanced Data Mining projects with R : Introduction to Neural Networks | packtpub.com
 
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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)
 
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Lecture 6 Business Data Mining (Artificial Neural Network and Support Vector Machine)
Views: 100 Phayung Meesad
Seminar on Neural Network - Datamining
 
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Presented by Karthik A
Views: 1156 Karthik Gowda
Tutorial RapidMiner Data Mining Neural Network
 
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Tutorial RapidMiner Data Mining Neural Network UNISNU Jepara Fakultas Sains dan Teknologi Program Studi Teknik Informatika
Views: 2850 Suharno Anakdesa
Neural Networks in R: Example with Categorical Response at Two Levels
 
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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
R-Session 11 - Statistical Learning - Neural Networks
 
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Source: neuralnet: Training of Neural Network by Frauke Gunther and Stefan Fritsch - The R Journal Vol. 2/1, June 2010
Views: 84861 Hamed Hasheminia
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: 23684 ProxyNotes
Tutorial Rapidminer Data Mining Neural Network Dataset Training and Scoring
 
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Tutorial Rapidminer Data Mining Neural Network (Dataset Training and Scoring)
Views: 6484 Wahyu adi putra
What is a Neural Network? | How Deep Neural Networks Work | Neural Network Tutorial | Simplilearn
 
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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
Artificial neural network prediction
 
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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
Data Mining- Forecasting using Neural Networks in RStudio
 
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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
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: 284133 Ryan Harris
Artificial Neural Network  in Hindi (Chapter 1 | Part1)
 
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Simplest explanation of Artificial Neural Network in Hindi
Views: 47728 Red Apple Tutorials
Weka Data Mining Tutorial for First Time & Beginner Users
 
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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
Neural Network Tutorial | Artificial Neural Network Tutorial | Deep Learning Tutorial | Simplilearn
 
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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
Data Science - Part VIII -  Artifical Neural Network
 
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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
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: 67736 Last moment tuitions
Data Mining with Weka - Neural Networks and Random Forests
 
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Simple introduction video on how to run neural networks and random forests in weka.
Views: 13022 Gaurav Jetley
IRIS Flower data set tutorial in artificial neural network in matlab
 
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Complete tutorial on http://www.techjatt.tk/2016/01/iris-flower-data-set-in-matlab-tutorial.html
Views: 45269 Tech Jatt
Neural Network Tutorial | Introduction to Neural Network | Deep Learning Tutorial - Part 1 | Edureka
 
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( 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
 
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SSAS - Data Mining - Decision Trees, Clustering, Neural networks
Views: 1460 M R Dhandhukia
Classification in Orange (CS2401)
 
24:02
A quick tutorial on analysing data in Orange using Classification.
Views: 47617 haikel5
Beginner Intro to Neural Networks 1: Data and Graphing
 
14:14
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
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: 21157 TensorFlow
Neural Networks in R | Arpan Gupta | Data Scientist & IITian
 
18:54
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
Introduction To Artificial Neural Network Explained With Example In Hindi
 
04:59
📚📚📚📚📚📚📚📚 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: 32316 5 Minutes Engineering
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: 97623 edureka!
Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5
 
13:02
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
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: 118343 sentdex
TensorFlow Tutorial #23 Time-Series Prediction
 
28:06
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
Machine Learning With Orange
 
05:21
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
Artificial Neural Network Tutorial Application Algorithm example ppt pdf in hindi | what is ANN urdu
 
28:16
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. ______________________________________________ Wanna Contact Me? Facebook: www.facebook.com/malikshahzaibofficial Insta: https://www.instagram.com/sirmalikshahzaib/ _________________________________________________________ Other Lectures: C++ lectures: http://y2u.be/cwZQecMC0Ik Games & Projects in c++: http://y2u.be/69lxVMY5610 Theory of Automata: http://y2u.be/4dlrMtv1j_M Software engineering: http://y2u.be/h7l6K181afo Operating System: http://y2u.be/9WxfkxcJ4DQ Socket Programing: http://y2u.be/uHqPzNg_0OE Artificial Intelligence: http://y2u.be/q1nmbfBR-44 what is ann Neural Network Tutorial neural network artificial intelligence tutorial artificial neural network in artificial intelligence I hope you like it. Thanks for watching !! _________________________________________________________________________________ For learning purposes join me on facebook: https://www.facebook.com/MalikShahzaibOfficial
Neural Network Explained -Artificial Intelligence - Hindi
 
03:52
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: 10561 CaelusBot
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: 72511 edureka!
Deep Neural Networks  with TensorFlow & Keras in R | Numeric Response Variable
 
17:28
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