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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/
Views: 65542 Great Learning
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
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: 922 Dvs Teja
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. Please write back to us at [email protected] or call us at +91 88808 62004 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 53896 edureka!
What is a Neural Network - Ep. 2 (Deep Learning SIMPLIFIED)
 
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With plenty of machine learning tools currently available, why would you ever choose an artificial neural network over all the rest? This clip and the next could open your eyes to their awesome capabilities! You'll get a closer look at neural nets without any of the math or code - just what they are and how they work. Soon you'll understand why they are such a powerful tool! Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Deep Learning is primarily about neural networks, where a network is an interconnected web of nodes and edges. Neural nets were designed to perform complex tasks, such as the task of placing objects into categories based on a few attributes. This process, known as classification, is the focus of our series. Classification involves taking a set of objects and some data features that describe them, and placing them into categories. This is done by a classifier which takes the data features as input and assigns a value (typically between 0 and 1) to each object; this is called firing or activation; a high score means one class and a low score means another. There are many different types of classifiers such as Logistic Regression, Support Vector Machine (SVM), and Naïve Bayes. If you have used any of these tools before, which one is your favorite? Please comment. Neural nets are highly structured networks, and have three kinds of layers - an input, an output, and so called hidden layers, which refer to any layers between the input and the output layers. Each node (also called a neuron) in the hidden and output layers has a classifier. The input neurons first receive the data features of the object. After processing the data, they send their output to the first hidden layer. The hidden layer processes this output and sends the results to the next hidden layer. This continues until the data reaches the final output layer, where the output value determines the object's classification. This entire process is known as Forward Propagation, or Forward prop. The scores at the output layer determine which class a set of inputs belongs to. Links: Michael Nielsen's book - http://neuralnetworksanddeeplearning.com/ Andrew Ng Machine Learning - https://www.coursera.org/learn/machine-learning Andrew Ng Deep Learning - https://www.coursera.org/specializations/deep-learning Have you worked with neural nets before? If not, is this clear so far? Please comment. Neural nets are sometimes called a Multilayer Perceptron or MLP. This is a little confusing since the perceptron refers to one of the original neural networks, which had limited activation capabilities. However, the term has stuck - your typical vanilla neural net is referred to as an MLP. Before a neuron fires its output to the next neuron in the network, it must first process the input. To do so, it performs a basic calculation with the input and two other numbers, referred to as the weight and the bias. These two numbers are changed as the neural network is trained on a set of test samples. If the accuracy is low, the weight and bias numbers are tweaked slightly until the accuracy slowly improves. Once the neural network is properly trained, its accuracy can be as high as 95%. Credits: Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 378508 DeepLearning.TV
Embeddings for Everything: Search in the Neural Network Era
 
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Dean's lecture, with Dan Gillick — Retrieval systems like internet search still use the same underlying keyword-based index they used back in the 1990s. Dan Gillick will describe his research on building a new kind of retrieval system based, somewhat unsurprisingly, on neural networks. He’ll try to explain the key pieces of technology and discuss how this may change the way we look for and find things. Dan Gillick is a research scientist at Google and teaches machine learning and natural language processing in the MIDS program.
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: 5749 Simplilearn
Data Mining Lecture -- Rule - Based Classification (Eng-Hindi)
 
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-~-~~-~~~-~~-~- Please watch: "PL vs FOL | Artificial Intelligence | (Eng-Hindi) | #3" https://www.youtube.com/watch?v=GS3HKR6CV8E -~-~~-~~~-~~-~-
Views: 30142 Well Academy
Create A Neural Network That Classifies Physical Activity Based On Smartphone Data
 
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Learn how to create a neural network with Keras and data from a smartphones accelerometer and gyroscope in order to determine what activity the user is engaging in. ► Subscribe To My New Artificial Intelligence Newsletter! https://goo.gl/qz1xeZ Code: https://github.com/jg-fisher/phoneMovementNeuralNetwork Dataset: https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones (you will find training and test data within a folder in the after unzipping) Keras Docs: https://keras.io/ -- Highly recommended for theoretical and applied ML -- Deep Learning: https://amzn.to/2LomU4y Hands on Machine Learning: https://amzn.to/2JSxhIv Hope you guys enjoyed this video! Be sure to leave any comments or questions below, thumbs up and subscribe for more neural networks and machine learning!
Views: 1614 John G. Fisher
Adversarial neural cryptography research - simple explanation
 
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Did it for my data mining class presentation. Research paper is available at: https://www.openreview.net/pdf?id=S1HEBe_Jl
Views: 310 johnnyg88
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: 1912 Suharno Anakdesa
Development of Intrusion Detection System Using Artificial Neural Network
 
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This Video gives the steps of developing an ANN model and the Development of ANN based Intrusion Detection System using KDD Cup 99 data along with the simulation results . Recorded using Screencast-O-Matic. This video lecture is prepared as a Resource Creation Assignment given in FDP programme on "Pedagogy for Online and Blended Teaching Learning Process" by IITBombay . It is also available in my moodle https://drpganeshkumarpdf.gnomio.com/
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: 21484 WekaMOOC
YOW! Data 2018 - Shujia Zhang - Graph Neural Networks: Algorithm and Applications #YOWData
 
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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: 632 YOW! Conferences
Data Mining Neural Network
 
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Video for UAS data Mining
Views: 98 Bagus Wira
INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS ANN IN HINDI
 
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Find the notes of ARTIFICIAL NEURAL NETWORKS in this link - https://viden.io/knowledge/artificial-neural-networks-ppt?utm_campaign=creator_campaign&utm_medium=referral&utm_source=youtube&utm_term=ajaze-khan-1
Views: 40393 LearnEveryone
Data Mining: Carvana Lemon Car Prediction using SAS Enterprise Miner
 
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Business Case: To predict if the car purchased at the Auction is a bad buy, using car related and purchase related data. Methods: Logistic regression, Decision Trees, Memory Based Reasoning, Neural Networks using SAS Enterprise Miner.
Views: 1568 Sachin's Tech Corner
Intelligent Heart Disease Prediction System Using Data Mining Techniques
 
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Neural Networks For Recommender Systems
 
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Recommender Systems are arguably the most common business application of Machine Learning systems. Recently a new blend of recommender systems have been developed by leveraging the tools and the modeling flexibility from the Deep Learning ecosystem. This presentation gives an overview of the main RecSys concepts such as matrix completion for collaborative filtering and relate those to current trends in Neural Network architectures. EVENT: dotAI 2017 SPEAKER: Olivier Grisel PERMISSIONS: The original video was published on dotconferences YouTube channel with the Creative Commons Attribution license (reuse allowed). ORIGINAL SOURCE: https://www.youtube.com/watch?v=HG3FDCegKVc&t=8s
Views: 5833 Coding Tech
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: 12280 Derek Kane
Trunk Branch Ensemble Convolutional Neural Networks for Video based Face Recognition
 
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Views: 8 MICANS VIDEOS
Seminar on Neural Network - Datamining
 
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Presented by Karthik A
Views: 984 Karthik Gowda
Text Analytics - Ep. 25 (Deep Learning SIMPLIFIED)
 
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Unstructured textual data is ubiquitous, but standard Natural Language Processing (NLP) techniques are often insufficient tools to properly analyze this data. Deep learning has the potential to improve these techniques and revolutionize the field of text analytics. Deep Learning TV on Facebook: https://www.facebook.com/DeepLearningTV/ Twitter: https://twitter.com/deeplearningtv Some of the key tools of NLP are lemmatization, named entity recognition, POS tagging, syntactic parsing, fact extraction, sentiment analysis, and machine translation. NLP tools typically model the probability that a language component (such as a word, phrase, or fact) will occur in a specific context. An example is the trigram model, which estimates the likelihood that three words will occur in a corpus. While these models can be useful, they have some limitations. Language is subjective, and the same words can convey completely different meanings. Sometimes even synonyms can differ in their precise connotation. NLP applications require manual curation, and this labor contributes to variable quality and consistency. Deep Learning can be used to overcome some of the limitations of NLP. Unlike traditional methods, Deep Learning does not use the components of natural language directly. Rather, a deep learning approach starts by intelligently mapping each language component to a vector. One particular way to vectorize a word is the “one-hot” representation. Each slot of the vector is a 0 or 1. However, one-hot vectors are extremely big. For example, the Google 1T corpus has a vocabulary with over 13 million words. One-hot vectors are often used alongside methods that support dimensionality reduction like the continuous bag of words model (CBOW). The CBOW model attempts to predict some word “w” by examining the set of words that surround it. A shallow neural net of three layers can be used for this task, with the input layer containing one-hot vectors of the surrounding words, and the output layer firing the prediction of the target word. The skip-gram model performs the reverse task by using the target to predict the surrounding words. In this case, the hidden layer will require fewer nodes since only the target node is used as input. Thus the activations of the hidden layer can be used as a substitute for the target word’s vector. Two popular tools: Word2Vec: https://code.google.com/archive/p/word2vec/ Glove: http://nlp.stanford.edu/projects/glove/ Word vectors can be used as inputs to a deep neural network in applications like syntactic parsing, machine translation, and sentiment analysis. Syntactic parsing can be performed with a recursive neural tensor network, or RNTN. An RNTN consists of a root node and two leaf nodes in a tree structure. Two words are placed into the net as input, with each leaf node receiving one word. The leaf nodes pass these to the root, which processes them and forms an intermediate parse. This process is repeated recursively until every word of the sentence has been input into the net. In practice, the recursion tends to be much more complicated since the RNTN will analyze all possible sub-parses, rather than just the next word in the sentence. As a result, the deep net would be able to analyze and score every possible syntactic parse. Recurrent nets are a powerful tool for machine translation. These nets work by reading in a sequence of inputs along with a time delay, and producing a sequence of outputs. With enough training, these nets can learn the inherent syntactic and semantic relationships of corpora spanning several human languages. As a result, they can properly map a sequence of words in one language to the proper sequence in another language. Richard Socher’s Ph.D. thesis included work on the sentiment analysis problem using an RNTN. He introduced the notion that sentiment, like syntax, is hierarchical in nature. This makes intuitive sense, since misplacing a single word can sometimes change the meaning of a sentence. Consider the following sentence, which has been adapted from his thesis: “He turned around a team otherwise known for overall bad temperament” In the above example, there are many words with negative sentiment, but the term “turned around” changes the entire sentiment of the sentence from negative to positive. A traditional sentiment analyzer would probably label the sentence as negative given the number of negative terms. However, a well-trained RNTN would be able to interpret the deep structure of the sentence and properly label it as positive. Credits Nickey Pickorita (YouTube art) - https://www.upwork.com/freelancers/~0147b8991909b20fca Isabel Descutner (Voice) - https://www.youtube.com/user/IsabelDescutner Dan Partynski (Copy Editing) - https://www.linkedin.com/in/danielpartynski Marek Scibior (Prezi creator, Illustrator) - http://brawuroweprezentacje.pl/ Jagannath Rajagopal (Creator, Producer and Director) - https://ca.linkedin.com/in/jagannathrajagopal
Views: 41069 DeepLearning.TV
More Data Mining with Weka (5.2: Multilayer Perceptrons)
 
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More Data Mining with Weka: online course from the University of Waikato Class 5 - Lesson 2: Multilayer Perceptrons 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: 28651 WekaMOOC
Devashish Shankar - Deep Learning for Natural Language Processing
 
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Much of the Text Mining needed in real-life boils down to Text Classification: be it prioritising e-mails received by Customer Care, categorising Tweets aired towards an Organisation, measuring impact of Promotions in Social Media, and (Aspect based) Sentiment Analysis of Reviews. These techniques can not only help gauge the customer’s feedback, but also can help in providing users a better experience. Traditional solutions focused on heavy domain-specific Feature Engineering, and thats exactly where Deep Learning sounds promising! We will depict our foray into Deep Learning with these classes of Applications in mind. Specifically, we will describe how we tamed Deep Convolutional Neural Network, most commonly applied to Computer Vision, to help classify (short) texts, attaining near-state-of-the-art results on several SemEval tasks consistently, and a few tasks of importance to Flipkart. In this talk, we plan to cover the following: Basics of Deep Learning as applied to NLP: Word Embeddings and its compositions a la Recursive Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. New Experimental results on an array of SemEval / Flipkart’s internal tasks: e.g. Tweet Classification and Sentiment Analysis. (As an example we achieved 95% accuracy in binary sentiment classification task on our datasets - up from 85% by statistical models) Share some of the learnings we have had while deploying these in Flipkart! Here is a mindmap explaining the flow of content and key takeawys for the audience: https://atlas.mindmup.com/2015/06/4cbcef50fa6901327cdf06dfaff79cf0/deep_learning_for_natural_language_proce/index.html We have decided to open source the code for this talk as a toolkit. https://github.com/flipkart-incubator/optimus Feel free to use it to train your own classifiers, and contribute!
Views: 11347 HasGeek TV
Processing our own Data - Deep Learning with Neural Networks and TensorFlow part 5
 
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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: 109293 sentdex
Machine Learning & Artificial Intelligence: Crash Course Computer Science #34
 
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So we've talked a lot in this series about how computers fetch and display data, but how do they make decisions on this data? From spam filters and self-driving cars, to cutting edge medical diagnosis and real-time language translation, there has been an increasing need for our computers to learn from data and apply that knowledge to make predictions and decisions. This is the heart of machine learning which sits inside the more ambitious goal of artificial intelligence. We may be a long way from self-aware computers that think just like us, but with advancements in deep learning and artificial neural networks our computers are becoming more powerful than ever. Produced in collaboration with PBS Digital Studios: http://youtube.com/pbsdigitalstudios Want to know more about Carrie Anne? https://about.me/carrieannephilbin The Latest from PBS Digital Studios: https://www.youtube.com/playlist?list=PL1mtdjDVOoOqJzeaJAV15Tq0tZ1vKj7ZV Want to find Crash Course elsewhere on the internet? Facebook - https://www.facebook.com/YouTubeCrash... Twitter - http://www.twitter.com/TheCrashCourse Tumblr - http://thecrashcourse.tumblr.com Support Crash Course on Patreon: http://patreon.com/crashcourse CC Kids: http://www.youtube.com/crashcoursekids
Views: 366086 CrashCourse
Data Mining Advanced Concept
 
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Convolutional Neural Network(CNN) combined with Artificial Neural Network(ANN) in order to predict a given image(Image Mining).
Views: 26 Niranjan Somasani
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. Please write back to us at [email protected] or call us at +91 88808 62004 for more information. Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka
Views: 4429 edureka!
Engine performance prediction using artificial neural network
 
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This video shows the script of Matlab file for engine performance prediction using artificial neural network (ANN) modelling. The input data for network training was obtained from engine laboratory testing. ANN prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm.
Views: 2041 othch01 othch01
Classification in Orange (CS2401)
 
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A quick tutorial on analysing data in Orange using Classification.
Views: 38899 haikel5
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: 5143 Wahyu adi putra
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: 140794 homevideotutor
Support Vector Machine (SVM) - Fun and Easy Machine Learning
 
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Support Vector Machine (SVM) - Fun and Easy Machine Learning https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. To understand SVM’s a bit better, Lets first take a look at why they are called support vector machines. So say we got some sample data over here of features that classify whether a observed picture is a dog or a cat, so we can for example look at snout length or and ear geometry if we assume that dogs generally have longer snouts and cat have much more pointy ear shapes. So how do we decide where to draw our decision boundary? Well we can draw it over here or here or like this. Any of these would be fine, but what would be the best? If we do not have the optimal decision boundary we could incorrectly mis-classify a dog with a cat. So if we draw an arbitrary separation line and we use intuition to draw it somewhere between this data point for the dog class and this data point of the cat class. These points are known as support Vectors – Which are defined as data points that the margin pushes up against or points that are closest to the opposing class. So the algorithm basically implies that only support vector are important whereas other training examples are ‘ignorable’. An example of this is so that if you have our case of a dog that looks like a cat or cat that is groomed like a dog, we want our classifier to look at these extremes and set our margins based on these support vectors. ----------- www.ArduinoStartups.com ----------- To learn more on Augmented Reality, IoT, Machine Learning FPGAs, Arduinos, PCB Design and Image Processing then Check out http://www.arduinostartups.com/ Please like and Subscribe for more videos :)
Views: 108816 Augmented Startups
Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition
 
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Trunk-Branch Ensemble Convolutional Neural Networks for Video-based Face Recognition - IEEE PROJECTS 2018 Download projects @ www.micansinfotech.com WWW.SOFTWAREPROJECTSCODE.COM https://www.facebook.com/MICANSPROJECTS Call: +91 90036 28940 ; +91 94435 11725 IEEE PROJECTS, IEEE PROJECTS IN CHENNAI,IEEE PROJECTS IN PONDICHERRY.IEEE PROJECTS 2018,IEEE PAPERS,IEEE PROJECT CODE,FINAL YEAR PROJECTS,ENGINEERING PROJECTS,PHP PROJECTS,PYTHON PROJECTS,NS2 PROJECTS,JAVA PROJECTS,DOT NET PROJECTS,IEEE PROJECTS TAMBARAM,HADOOP PROJECTS,BIG DATA PROJECTS,Signal processing,circuits system for video technology,cybernetics system,information forensic and security,remote sensing,fuzzy and intelligent system,parallel and distributed system,biomedical and health informatics,medical image processing,CLOUD COMPUTING, NETWORK AND SERVICE MANAGEMENT,SOFTWARE ENGINEERING,DATA MINING,NETWORKING ,SECURE COMPUTING,CYBERSECURITY,MOBILE COMPUTING, NETWORK SECURITY,INTELLIGENT TRANSPORTATION SYSTEMS,NEURAL NETWORK,INFORMATION AND SECURITY SYSTEM,INFORMATION FORENSICS AND SECURITY,NETWORK,SOCIAL NETWORK,BIG DATA,CONSUMER ELECTRONICS,INDUSTRIAL ELECTRONICS,PARALLEL AND DISTRIBUTED SYSTEMS,COMPUTER-BASED MEDICAL SYSTEMS (CBMS),PATTERN ANALYSIS AND MACHINE INTELLIGENCE,SOFTWARE ENGINEERING,COMPUTER GRAPHICS, INFORMATION AND COMMUNICATION SYSTEM,SERVICES COMPUTING,INTERNET OF THINGS JOURNAL,MULTIMEDIA,WIRELESS COMMUNICATIONS,IMAGE PROCESSING,IEEE SYSTEMS JOURNAL,CYBER-PHYSICAL-SOCIAL COMPUTING AND NETWORKING,DIGITAL FORENSIC,DEPENDABLE AND SECURE COMPUTING,AI - MACHINE LEARNING (ML),AI - DEEP LEARNING ,AI - NATURAL LANGUAGE PROCESSING ( NLP ),AI - VISION (IMAGE PROCESSING),mca project PARALLEL AND DISTRIBUTED SYSTEMS 1. Enhancing Collusion Resilience in Reputation Systems 2. A Crowdsourcing Worker Quality Evaluation Algorithm on MapReduce for Big Data Applications 3. Evaluating Replication for Parallel Jobs: An Efficient Approach 4. Conditions and Patterns for Achieving Convergence in OT-Based Co-Editors 5. Prefetching on Storage Servers through Mining Access Patterns on Blocks 6. SPA: A Secure and Private Auction Framework for Decentralized Online Social Networks 7. Predicting Cross-Core Performance Interference on Multicore Processors with Regression Analysis 8. Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds 9. RFHOC: A Random-Forest Approach to Auto-Tuning Hadoop's Configuration 10. Deadline Guaranteed Service for Multi-Tenant Cloud Storage 11. Carbon-Aware Online Control of Geo-Distributed Cloud Services 12. Online Resource Scheduling Under Concave Pricing for Cloud Computing 13. Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds 14. Performance Evaluation of Cloud Computing Centers with General Arrivals and Service 15. TMACS: A Robust and Verifiable Threshold Multi-Authority Access Control System in Public Cloud Storage 16. Heads-Join: Efficient Earth Mover's Distance Similarity Joins on Hadoop 17. Enabling Personalized Search over Encrypted Outsourced Data with Efficiency Improvement 18. Quantum-Inspired Hyper-Heuristics for Energy-Aware Scheduling on Heterogeneous Computing Systems 19. A Secure and Dynamic Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data 20. A High Performance Parallel and Heterogeneous Approach to Narrowband Beamforming 21. EcoUp: Towards Economical Datacenter Upgrading 22. Hadoop Performance Modeling for Job Estimation and Resource Provisioning 23. Optimization of the Processing of Data Streams on Roughly Characterized Distributed Resources 24. A Secure Anti-Collusion Data Sharing Scheme for Dynamic Groups in the Cloud 25. Exploring Heterogeneity within a Core for Improved Power Efficiency 26. Efficient File Search in Delay Tolerant Networks with Social Content and Contact Awareness 27. Exploiting Workload Characteristics and Service Diversity to Improve the Availability of Cloud Storage Systems 28. PerfCompass: Online Performance Anomaly Fault Localization and Inference in Infrastructure-as-a-Service Clouds 29. Energy and Makespan Tradeoffs in Heterogeneous Computing Systems using Efficient Linear Programming Techniques 30. An Efficient Privacy-Preserving Ranked Keyword Search Method 31. GrapH: Traffic-Aware Graph Processing (June 1 2018) 32. Automatic construction of vertical search tools for the Deep Web (Feb. 2018) 33. Towards Long-View Computing Load Balancing in Cluster Storage Systems 34. ATOM: Efficient Tracking, Monitoring, and Orchestration of Cloud Resources 35. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds 36. Repair Tree: Fast Repair for Single Failure in Erasure-coded Distributed Storage Systems 37. A Load Balancing and Multi-tenancy Oriented Data Center Virtualization Framework 38. Stochastic Resource Provisioning for Containerized Multi-Tier Web Services in Clouds
How kNN algorithm works
 
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In this video I describe how the k Nearest Neighbors algorithm works, and provide a simple example using 2-dimensional data and k = 3. This presentation is available at: http://prezi.com/ukps8hzjizqw/?utm_campaign=share&utm_medium=copy
Views: 361608 Thales Sehn Körting
REND: A Reinforced Network-Based Model for Clustering Sparse Data with Application to...
 
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REND: A Reinforced Network-Based Model for Clustering Sparse Data with Application to Cancer Subtype Discovery [full title] Prof. Wei Ding, UMass Boston Abstract: We will discuss a new algorithm, called Reinforced Network-Based Model for Clustering Sparse Data (REND), for finding unknown groups of similar data objects in sparse and largely non-overlapping feature space where a network structure among features can be observed. REND is an autoencoder neural network alternative to non-negative matrix factorization (NMF). NMF has made significant advancements in various clustering tasks with great practical success. The use of neural networks over NMF allows the implementation of non-negative model variants with multi-layered, arbitrarily non-linear structures, which is much needed to handle nonlinearity in complex real data. However, standard neural networks cannot achieve its full potential when data is sparse and the sample size is hundreds of orders of magnitude smaller than the dimension of the feature space. To address these issues, we present a model consisting of integrated layers of reinforced network smoothing and an sparse autoencoder. The architecture of hidden layers incorporates existing network dependency in the feature space. The reinforced network layers smooth sparse data over the network structure. Most importantly, through backpropagation, the weights of the reinforced smoothing layers are simultaneously constrained by the remaining sparse autoencoder layers that set the target values to be equal to the inputs. Our approach integrates physically meaningful feature dependencies into model design and efficiently clusters sparse data through integrated smoothing and sparse autoencoder learning. Empirical results demonstrate that REND achieves improved accuracy and render physically meaningful clustering results. Speaker Bio: Wei Ding received her Ph.D. degree in Computer Science from the University of Houston in 2008. She is an Associate Professor of Computer Science at the University of Massachusetts Boston. Her research interests include data mining, machine learning, artificial intelligence, computational semantics, and with applications to health sciences, astronomy, geosciences, and environmental sciences. She has published more than 122 referred research papers, 1 book, and has 2 patents. She is an Associate Editor of the ACM Transaction on Knowledge Discovery from Data (TKDD), Knowledge and Information Systems (KAIS) and an editorial board member of the Journal of Information System Education (JISE), the Journal of Big Data, and the Social Network Analysis and Mining Journal. Her research projects are sponsored by NSF, NIH, NASA, and DOE. She is an IEEE senior member and an ACM senior member.
How CNN (Convolutional Neural Networks - Deep Learning) algorithm works
 
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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: http://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: 20772 Thales Sehn Körting
Predicting Instructor Performance Using Data Mining Techniques in Higher Education
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. In general, research in educational mining focuses on modeling student's performance instead of instructors' performance. One of the common tools to evaluate instructors' performance is the course evaluation questionnaire to evaluate based on students' perception. In this paper, four different classication techniquesdecision tree algorithms, support vector machines, articial neural networks, and discriminant analysisare used to build classier models. Their performances are compared over a data set composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specicity performance metrics. Although all the classier models show comparably high classication performances, C5.0 classier is the best with respect to accuracy, precision, and specicity. In addition, an analysis of the variable importance for each classier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors' success based on the students' perception mainly depends on the interest of the students in the course. The ndings of this paper indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these ndings may be used to improve the measurement instruments. Articial neural networks, classication algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines. -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Student Learning Evaluation - Predicting Student Performance
 
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Predicting Instructor Performance Using Data Mining Techniques in Higher Education -- Data mining applications are becoming a more common tool in understanding and solving educational and administrative problems in higher education. Generally, research in educational mining focuses on modeling student’s performance instead of instructors’ performance. One of the common tools to evaluate instructors’ performance is the course evaluation questionnaire to evaluate based on students’ perception. In this study, four different classification techniques, –decision tree algorithms, support vector machines, artificial neural networks, and discriminant analysis– are used to build classifier models. Their performances are compared over a dataset composed of responses of students to a real course evaluation questionnaire using accuracy, precision, recall, and specificity performance metrics. Although all the classifier models show comparably high classification performances, C5.0 classifier is the best with respect to accuracy, precision, and specificity. In addition, an analysis of the variable importance for each classifier model is done. Accordingly, it is shown that many of the questions in the course evaluation questionnaire appear to be irrelevant. Furthermore, the analysis shows that the instructors’ success based on the students’ perception mainly depends on the interest of the students in the course. The findings of the study indicate the effectiveness and expressiveness of data mining models in course evaluation and higher education mining. Moreover, these findings may be used to improve measurement instruments. Artificial neural networks, classification algorithms, decision trees, linear discriminant analysis, performance evaluation, support vector machines -- For More Details Contact Us -- S.Venkatesan Arihant Techno Solutions Pudukkottai www.arihants.com Mobile: +91 75984 92789
Neural Network Performance Prediction for Early Stopping
 
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In the neural network domain, methods for hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of neural network configurations. In this paper, we show that a simple regression model, based on support vector machines, can predict the final performance of partially trained neural network configurations using features based on network architectures, hyperparameters, and time-series validation performance data. We use this regression model to develop an early stopping strategy for neural network configurations. With this early stopping strategy, we obtain significant speedups in both hyperparameter optimization and meta-modeling. Particularly in the context of meta-modeling, our method can learn to predict the performance of drastically different architectures and is seamlessly incorporated into reinforcement learning-based architecture selection algorithms. Finally, we show that our method is simpler, faster, and more accurate than Bayesian methods for learning curve prediction.
Views: 446 Macgyver
Joe Jevnik - A Worked Example of Using Neural Networks for Time Series Prediction
 
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PyData New York City 2017 Slides: https://github.com/llllllllll/osu-talk Most neural network examples and tutorials use fake data or present poorly performing models. In this talk, we will walk through the process of implementing a real model, starting from the beginning with data collection and cleaning. We will cover topics like feature selection, window normalization, and feature scaling. We will also present development tips for testing and deploying models.
Views: 9933 PyData
Electricity Load Forecasting with the help of Artificial Neural Network in matlab
 
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One day ahead electricity load forecasting in Matlab with the help of the Artificial neural network. For more information visit our website - www.matlabsolutions.com
Views: 8544 MATLAB Solutions
Exploring Whole Brain fMRI Data with Unsupervised Artificial Neural Networks   YouTube
 
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Machine Learning in Health Care
 
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Analysis of medical images is essential in modern medicine. With the ever-increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment, and monitoring. The InnerEye research project focuses on the automatic analysis of patients' medical scans. It uses state-of-the-art machine learning techniques for the: •Automatic delineation and measurement of healthy anatomy and anomalies; •Robust registration for monitoring disease progression; •Semantic navigation and visualization for improved clinical workflow; •Development of natural user interfaces for medical practitioners.
Views: 27155 Microsoft Research
How to Make a Text Summarizer - Intro to Deep Learning #10
 
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I'll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We'll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory. Code for this video (Challenge included): https://github.com/llSourcell/How_to_make_a_text_summarizer Jie's Winning Code: https://github.com/jiexunsee/rudimentary-ai-composer More Learning resources: https://www.quora.com/Has-Deep-Learning-been-applied-to-automatic-text-summarization-successfully https://research.googleblog.com/2016/08/text-summarization-with-tensorflow.html https://en.wikipedia.org/wiki/Automatic_summarization http://deeplearning.net/tutorial/rnnslu.html http://machinelearningmastery.com/text-generation-lstm-recurrent-neural-networks-python-keras/ Please subscribe! And like. And comment. That's what keeps me going. Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/ Signup for my newsletter for exciting updates in the field of AI: https://goo.gl/FZzJ5w
Views: 136349 Siraj Raval

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