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Simple & Delicious – October/November 2018-P2P

Taste of Home Simple & Delicious is America’s fast cooking magazine brought to you by the editors of America’s #1 Cooking Magazine, Taste of Home! Packed with 150+ recipes and tips in every issue, you’ll enjoy tons of easy, irresistable dishes and fast-to-fix meal ideas…a full color photo of every finished recipe…and so much more!

Simple & Delicious – October/November 2018-P2P
English |190 pages | PDF | 18 MB
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110% Gaming – Issue 53 2018-P2P

110% Gaming is an exciting new gaming magazine that’s packed with news and reviews, online LOLs, hints and tips, puzzles, comps and more!

110% Gaming – Issue 53 2018-P2P
English |190 pages | PDF | 12 MB
DOWNLOAD: SUPRAFILESDOUPLOADS

BBC Focus – November 2018-P2P

BBC Focus magazine is a science technology magazine with a trained eye on the future. Includes scientific breakthroughs and gadgets that could shape the future of the human race, on earth or in space.

BBC Focus – November 2018-P2P
English | 116 pages | True PDF | 18.5 MB
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Men’s Fitness UK – December 2018-P2P

A lifestyle magazine dedicated to showing men the practical and positive actions that make their lives better, with articles covering fitness, relationships, nutrition, careers, grooming, travel and health issues.

Men’s Fitness UK – December 2018-P2P
English | 116 pages | True PDF | 18.8 MB
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WebUser – Issue 460 (17-30 October) 2018-P2P

WebUser is the UK’s favourite internet magazine. On sale every fortnight it keeps you up-to-date with all the latest news, views, best new websites, music, film and games downloads, free software, and all the other developments on the Web. If you use the internet, you’ll love WebUser. Being Britain’s best-selling internet read, WebUser is, quite simply, the only internet magazine you’ll ever need.

WebUser – Issue 460 (17-30 October) 2018-P2P
English | 76 pages | True PDF | 9.4 MB
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Hands-On Markov Models with Python-P2P

Hidden Markov Model (HMM) is a statistical model based on the Markov chain concept. Hands-On Markov Models with Python helps you get to grips with HMMs and different inference algorithms by working on real-world problems. The hands-on examples explored in the book help you simplify the process flow in machine learning by using Markov model concepts, thereby making it accessible to everyone.

Once you’ve covered the basic concepts of Markov chains, you’ll get insights into Markov processes, models, and types with the help of practical examples. After grasping these fundamentals, you’ll move on to learning about the different algorithms used in inferences and applying them in state and parameter inference. In addition to this, you’ll explore the Bayesian approach of inference and learn how to apply it in HMMs.

In further chapters, you’ll discover how to use HMMs in time series analysis and natural language processing (NLP) using Python. You’ll also learn to apply HMM to image processing using 2D-HMM to segment images. Finally, you’ll understand how to apply HMM for reinforcement learning (RL) with the help of Q-Learning, and use this technique for single-stock and multi-stock algorithmic trading.

By the end of this book, you will have grasped how to build your own Markov and hidden Markov models on complex datasets in order to apply them to projects.

Hands-On Markov Models with Python-P2P
by Ankur Ankan, Abinash Panda
English | October 9th, 2018 | ISBN: 1788625447 | 178 Pages | PDF/EPUB | 41.7 MB/24.4 MB
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Five Photons: Remarkable Journeys of Light Across Space and Time-P2P

The story of the universe is written in the light that travels through it—the light that we can capture, that is. Nearly everything we know about how the universe works on its grandest scale comes from the analysis of light, of photons that may have journeyed for nearly fourteen billion years to reach us from the Big Bang itself. In Five Photons, astrophysicist James Geach serves as our guide on this cosmic voyage.

Five Photons: Remarkable Journeys of Light Across Space and Time-P2P
English | October 15th, 2018 | ISBN: 1780239912 | 192 Pages | EPUB | 4.35 MB
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