Learning To Predict And Predicting To Learn
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Learning To Predict And Predicting To Learn
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Author : Thomas DeVere Wolsey
language : en
Publisher: Prentice Hall
Release Date : 2009
Learning To Predict And Predicting To Learn written by Thomas DeVere Wolsey and has been published by Prentice Hall this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009 with Education categories.
Featuring practical instructional routines that are clearly linked to cognitive strategies students need to make sense of text, this book combines a rationale written from the perspective of current research that supports the use of the strategy or instructional routine with clear step-by-step directions and multiple examples from the classroom experiences of teachers across the United States. These experiences appear as boxed features that are easily identifiable by the reader. The text is written in such a way that readers may start on page one and work through the end of the book or use the book as a reference for their own practice or as an inservice tool. Each cognitive strategy is linked via convenient matrices to the instructional routines that promote precision thinking on the part of students. Features: Differentiation between cognitive strategies for students and instructional routines teachers might use. Provides teachers and preservice teachers with a means to think about the tools they use to promote cognitive proficiency on the part of students. Often, strategies are used a catch-all term that does not clarify the difference between what teachers do and how students incorporate learn from those routines. Boxed features: Real teachers’ explain how they have used the tools discussed in the book. Provides teachers with examples to which they may be able to relate. Instead of an isolated example, the voices of classroom teachers will explain how they have implemented instructional routines or promoted cognitive strategies for their students. Sound rationale coupled with step-by-step procedures. Teachers often like to know what works, but many texts ignore their need and desire to know why a strategy or routine works. This text links rationale with tools so that readers will be able to explain why they are using a routine or assisting students to use cognitive tools to understand how they might think more precisely about the books they read. Theme: Prediction. Prediction is a popular request teachers make of their students, but often teachers lack sufficient experience or rationale to know how students might use prediction to increase precision in thinking about books and other texts they read. Approach: Combination of both theoretical and research with useful tools students and teachers can implement tomorrow. Many books take either a theoretical approach with little classroom application provided or a practical approach that does not help teachers understand why a given tool is useful and under what circumstances. This book combines the best of both approaches to help teacher-readers understand why a strategy or routine is worth the instructional time that might be devoted to it.
Prediction Learning And Games
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Author : Nicolo Cesa-Bianchi
language : en
Publisher: Cambridge University Press
Release Date : 2006-03-13
Prediction Learning And Games written by Nicolo Cesa-Bianchi and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-03-13 with Computers categories.
This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual sequences does not impose any probabilistic assumption on the data-generating mechanism. Yet, prediction algorithms can be constructed that work well for all possible sequences, in the sense that their performance is always nearly as good as the best forecasting strategy in a given reference class. The central theme is the model of prediction using expert advice, a general framework within which many related problems can be cast and discussed. Repeated game playing, adaptive data compression, sequential investment in the stock market, sequential pattern analysis, and several other problems are viewed as instances of the experts' framework and analyzed from a common nonstochastic standpoint that often reveals new and intriguing connections.
Frequency In Language
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Author : Dagmar Divjak
language : en
Publisher: Cambridge University Press
Release Date : 2019-10-10
Frequency In Language written by Dagmar Divjak and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-10 with Language Arts & Disciplines categories.
Re-examines frequency, entrenchment and salience, three foundational concepts in usage-based linguistics, through the prism of learning, memory, and attention.
Prediction In Second Language Processing And Learning
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Author : Edith Kaan
language : en
Publisher: John Benjamins Publishing Company
Release Date : 2021-09-15
Prediction In Second Language Processing And Learning written by Edith Kaan and has been published by John Benjamins Publishing Company this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-15 with Language Arts & Disciplines categories.
There is ample evidence that language users, including second-language (L2) users, can predict upcoming information during listening and reading. Yet it is still unclear when, how, and why language users engage in prediction, and what the relation is between prediction and learning. This volume presents a collection of current research, insights, and directions regarding the role of prediction in L2 processing and learning. The contributions in this volume specifically address how different (L1-based) theoretical models of prediction apply to or may be expanded to account for L2 processing, report new insights on factors (linguistic, cognitive, social) that modulate L2 users’ engagement in prediction, and discuss the functions that prediction may or may not serve in L2 processing and learning. Taken together, this volume illustrates various fruitful approaches to investigating and accounting for differences in predictive processing within and across individuals, as well as across populations.
Predictive Analytics
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Author : Richard Hurley
language : en
Publisher:
Release Date : 2019-12-30
Predictive Analytics written by Richard Hurley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-12-30 with categories.
If you want to learn about predictive analytics without having to read a boring textbook, then keep reading... Companies are collecting more data from ever. With the ease of collecting all that data, all the different sources where you can receive the data, and the inexpensive storage, it makes sense to collect as much data as possible. But without a good analysis of that data, and without some time to really figure out what trends and insights are inside all of it, that data becomes worthless. This is where predictive analytics is going to come in handy. You will be able to actually take all of the data that you have been collecting and storing, and see what insights are in there to lead some of your business decisions in the future. This guidebook is going to look at predictive analytics, and some of the topics we will explore concerning this topic include: The basics of predictive analysis. How to predict events that are going to happen in the future with big data and data mining. How to predict events that are going to happen in the future with the help of data analysis and statistics. A look at machine learning and how this process can help make predictions. How to avoid prediction traps, avoid bias, and make the best decisions with this analysis. Some of the top reasons to implement this kind of analysis in your business. The steps you can take to create your own predictive analysis model. And much, much more! Working on predictive analytics is going to be one of the best ways that your business can use the data you have to look more deeply inside, and sort through the different predictions you can make. Click the "add to cart" button to start your learning!
Predictive Analytics
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Author : Eric Siegel
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-20
Predictive Analytics written by Eric Siegel and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-20 with Business & Economics categories.
"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics(aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.
Incremental Learning For Motion Prediction Of Pedestrians And Vehicles
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Author : Alejandro Dizan Vasquez Govea
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-23
Incremental Learning For Motion Prediction Of Pedestrians And Vehicles written by Alejandro Dizan Vasquez Govea and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-23 with Technology & Engineering categories.
This book focuses on the problem of moving in a cluttered environment with pedestrians and vehicles. A framework based on Hidden Markov models is developed to learn typical motion patterns which can be used to predict motion on the basis of sensor data.
Duck On A Bike
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Author : David Shannon
language : en
Publisher: Scholastic Inc.
Release Date : 2016-07-26
Duck On A Bike written by David Shannon and has been published by Scholastic Inc. this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-07-26 with Juvenile Fiction categories.
In this off-beat book perfect for reading aloud, a Caldecott Honor winner shares the story of a duck who rides a bike with hilarious results. One day down on the farm, Duck got a wild idea. “I bet I could ride a bike,” he thought. He waddled over to where the boy parked his bike, climbed on, and began to ride. At first, he rode slowly and he wobbled a lot, but it was fun! Duck rode past Cow and waved to her. “Hello, Cow!” said Duck. “Moo,” said Cow. But what she thought was, “A duck on a bike? That’s the silliest thing I’ve ever seen!” And so, Duck rides past Sheep, Horse, and all the other barnyard animals. Suddenly, a group of kids ride by on their bikes and run into the farmhouse, leaving the bikes outside. Now ALL the animals can ride bikes, just like Duck! Praise for Duck on a Bike “Shannon serves up a sunny blend of humor and action in this delightful tale of a Duck who spies a red bicycle one day and gets “a wild idea” . . . Add to all this the abundant opportunity for youngsters to chime in with barnyard responses (“M-o-o-o”; “Cluck! Cluck!”), and the result is one swell read-aloud, packed with freewheeling fun.” —Publishers Weekly “Grab your funny bone—Shannon . . . rides again! . . . A “quackerjack” of a terrific escapade.” —Kirkus Reviews
International Handbook Of Emotions In Education
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Author : Reinhard Pekrun
language : en
Publisher: Routledge
Release Date : 2014-04-16
International Handbook Of Emotions In Education written by Reinhard Pekrun and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-16 with Education categories.
For more than a decade, there has been growing interest and research on the pivotal role of emotions in educational settings. This ground-breaking handbook is the first to highlight this emerging field of research and to describe in detail the ways in which emotions affect learning and instruction in the classroom as well as students’ and teachers’ development and well-being. Informed by research from a number of related fields, the handbook includes four sections. Section I focuses on fundamental principles of emotion, including the interplay among emotion, cognition, and motivation, the regulation of emotion, and emotional intelligence. Section II examines emotions and emotion regulation in classroom settings, addressing specific emotions (enjoyment, interest, curiosity, pride, anxiety, confusion, shame, and boredom) as well as social-emotional learning programs. Section III highlights research on emotions in academic content domains (mathematics, science, and reading/writing), contextual factors (classroom, family, and culture), and teacher emotions. The final section examines the various methodological approaches to studying emotions in educational settings. With work from leading international experts across disciplines, this book synthesizes the latest research on emotions in education.
Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-07-15
Stroke Analysis And Prediction Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-07-15 with Computers categories.
In this project, we will perform an analysis and prediction task on stroke data using machine learning and deep learning techniques. The entire process will be implemented with Python GUI for a user-friendly experience. We start by exploring the stroke dataset, which contains information about various factors related to individuals and their likelihood of experiencing a stroke. We load the dataset and examine its structure, features, and statistical summary. Next, we preprocess the data to ensure its suitability for training machine learning models. This involves handling missing values, encoding categorical variables, and scaling numerical features. We utilize techniques such as data imputation and label encoding. To gain insights from the data, we visualize its distribution and relationships between variables. We create plots such as histograms, scatter plots, and correlation matrices to understand the patterns and correlations in the data. To improve model performance and reduce dimensionality, we select the most relevant features for prediction. We employ techniques such as correlation analysis, feature importance ranking, and domain knowledge to identify the key predictors of stroke. Before training our models, we split the dataset into training and testing subsets. The training set will be used to train the models, while the testing set will evaluate their performance on unseen data. We construct several machine learning models to predict stroke. These models include Support Vector, Logistic Regression, K-Nearest Neighbors (KNN), Decision Tree, Random Forest, Gradient Boosting, Light Gradient Boosting, Naive Bayes, Adaboost, and XGBoost. Each model is built and trained using the training dataset. We train each model on the training dataset and evaluate its performance using appropriate metrics such as accuracy, precision, recall, and F1-score. This helps us assess how well the models can predict stroke based on the given features. To optimize the models' performance, we perform hyperparameter tuning using techniques like grid search or randomized search. This involves systematically exploring different combinations of hyperparameters to find the best configuration for each model. After training and tuning the models, we save them to disk using joblib. This allows us to reuse the trained models for future predictions without having to train them again. With the models trained and saved, we move on to implementing the Python GUI. We utilize PyQt libraries to create an interactive graphical user interface that provides a seamless user experience. The GUI consists of various components such as buttons, checkboxes, input fields, and plots. These components allow users to interact with the application, select prediction models, and visualize the results. In addition to the machine learning models, we also implement an ANN using TensorFlow. The ANN is trained on the preprocessed dataset, and its architecture consists of a dense layer with a sigmoid activation function. We train the ANN on the training dataset, monitoring its performance using metrics like loss and accuracy. We visualize the training progress by plotting the loss and accuracy curves over epochs. Once the ANN is trained, we save the model to disk using the h5 format. This allows us to load the trained ANN for future predictions. In the GUI, users have the option to choose the ANN as the prediction model. When selected, the ANN model is loaded from disk, and predictions are made on the testing dataset. The predicted labels are compared with the true labels for evaluation. To assess the accuracy of the ANN predictions, we calculate various evaluation metrics such as accuracy score, precision, recall, and classification report. These metrics provide insights into the ANN's performance in predicting stroke. We create plots to visualize the results of the ANN predictions. These plots include a comparison of the true values and predicted values, as well as a confusion matrix to analyze the classification accuracy. The training history of the ANN, including the loss and accuracy curves over epochs, is plotted and displayed in the GUI. This allows users to understand how the model's performance improved during training. In summary, this project covers the analysis and prediction of stroke using machine learning and deep learning models. It encompasses data exploration, preprocessing, model training, hyperparameter tuning, GUI implementation, ANN training, and prediction visualization. The Python GUI enhances the user experience by providing an interactive and intuitive platform for exploring and predicting stroke based on various features.