Tag: Learning

Machine Learning for Beginners Master Fundamentals in NLP, ML Algorithms, Deep Learning, and More

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Free Download Machine Learning for Beginners: Master Fundamentals in NLP, ML Algorithms, Deep Learning, and More with This Simple Introductory Guide. Learn ML Techniques in Less Than 14 Days by Cobas Inkworks
English | August 9, 2024 | ISBN: N/A | ASIN: B0CWDXTS4Y | 224 pages | EPUB | 1.39 Mb
📘 Ignite Your Machine Learning Potential![/center]
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Hands-On Machine Learning with C++, 2nd Edition


Free Download Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines, 2nd Edition by Kirill Kolodiazhnyi
English | January 24th, 2025 | ISBN: 1805120573 | 512 pages | True PDF | 13.23 MB
Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets

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Hands-On Machine Learning with C++ Build, train and deploy end-to-end machine learning and deep learning pipelines 2nd Edition


Free Download Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines, 2nd Edition by Kirill Kolodiazhnyi
English | January 24, 2025 | ISBN: 1805120573 | True EPUB | 512 pages | 16.8 MB
Apply supervised and unsupervised machine learning algorithms using C++ libraries, such as PyTorch C++ API, Flashlight, Blaze, mlpack, and dlib using real-world examples and datasets

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E-Collaborative Knowledge Construction Learning from Computer-Supported and Virtual Environments


Free Download Bernhard Ertl, "E-Collaborative Knowledge Construction: Learning from Computer-Supported and Virtual Environments"
English | 2010 | pages: 360 | ISBN: 1615207295 | PDF | 3,5 mb
In today’s society, the quantity of information available to learners is so vast that new strategies of information processing and exchange must be continually developed and improved. E-Collaborative Knowledge Construction: Learning from Computer-Supported and Virtual Environments explores the construction of beneficial e-collaborative knowledge environments from four vital perspectives: educational, psychological, organizational, and technical. It offers several scenarios where the implementation of e-collaborative knowledge construction is necessary and then not only presents methods for facilitating e-collaborative knowledge construction, but also provides methods for assessing its results. This exciting new publication is a must-have for academics, researchers, and professionals who dare to discover new innovations!

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Serverless Machine Learning with Amazon Redshift Create, train, and deploy machine learning models using familiar


Free Download Serverless Machine Learning with Amazon Redshift: Create, train, and deploy machine learning models using familiar SQL commands by Debabrata Panda, Phil Bates, Bhanu Pittampally
English | September 11, 2023 | ISBN: 1804619280 | 384 pages | EPUB | 14 Mb
Supercharge and deploy Amazon Redshift Serverless, train and deploy Machine learning Models using Amazon Redshift ML and run inference queries at scale.

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Regression and Machine Learning for Education Sciences Using R


Free Download Regression and Machine Learning for Education Sciences Using R by Cody Dingsen
English | November 1, 2024 | ISBN: 1032510072 | 360 pages | MOBI | 33 Mb
This book provides a conceptual introduction to regression analysis and machine learning and their applications in education research. It discusses their diverse applications, including its role in predicting future events based on the current data or explaining why some phenomena occur. These identified important predictors provide data-based evidence for educational and psychological decision-making.

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Organizing Schools for Productive Learning


Free Download Shlomo Sharan, Ivy Geok Chin Tan, "Organizing Schools for Productive Learning"
English | 2008 | pages: 117 | ISBN: 1402083947 | PDF | 0,6 mb
A major problem confronting schools is that many students are turned off from learning and are bored. Boredom is destructive of learning. The No Child Left Behind (NCLB) initiative of the US government (2001) stemmed from the claim – accompanied by sharp debates pro and con – that many schools in the United States fail to achieve basic educational objectives, and that many schools are doing a poor job for a wide variety of reasons and surely not just because of student boredom (Brigham, Gustashaw, Wiley, & Brigham, 2004; Essex, 2006; Goodman, Shannon, Goodman, & Rapoport, 2004; Sunderman, Tracey Jr. , Kim, & Orfield, 2004). The model of school organization and instruction presented here seeks to provide an effective plan for significant improvement in secondary school education, one of whose central aims is to make students genuinely engaged in what they are learning. The NCLB legislation emphasizes, inter alia, the need for school improvement. Without it one cannot reasonably anticipate improvement over current levels in student engagement in learning and in academic achievement. The NCLB literature frequently employs the term "school improvement" to refer to the quality of the teachers, such as their academic credentials, instructional competence, and their knowledge of subject matter. Similarly, "school restructuring" is said to include steps such as transforming the school into a charter school, replacing the teaching staff, or inviting a private company to administer the school. The use of those terms in this work is distinctly different.

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