Book Review


Big Data Analysis using ensemble machine learning of scikit-learn in Python

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In this book, scikit-learn which is the best open source machine learning library is introduced for businessman or businesswoman and used in Python for big data analysis. Three examples are illustrated how to use ensemble machine learning for big data analysis. In the first example, rules between poker hands (five cards) and their rankings can be trained using ensemble machine learning. The illustrated ensemble machine learning includes AdaBoost, Bagging, ExtraTrees, GradientBoosting, and RandomForest.


Artificial Intelligence for Games: Ian Millington, John Funge: 8601300089652: Amazon.com: Books

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The vast majority of software development books, whether it be for line-of-business app dev or game development, seem to have little to no information that can be found via a casual internet search. There is a refreshing breadth and depth of game AI knowledge in this book that has been of tremendous help. My only complaints are that the pseudocode seems to be overly simplified and not as easily converted to a concrete implementation as I'd like, and that even for a book on game-specific AI implementations, the authors seem to enjoy a bit more of an academic/idealized approach to the design. That might be less bothersome to a professional game developer, but I'm at the hobbyist/indie level, and sometimes need a quick-and-dirty implementation before I begin to really understand what's going on.


Free Must Read Books on Statistics & Mathematics for Data Science

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The selection process of data scientists at Google gives higher priority to candidates with strong background in statistics and mathematics. This is a highly recommended book for practicing data scientists. Along with derivations & practice example, this book has dedicated sections of calculus, algebra, probability etc. After you finish with essentials of mathematics, this book will help you connect various theorem and algorithm quickly with their formulae.


[N] Early access to deep learning book by Keras author • r/MachineLearning

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To me, this honestly seems like him riding the popularity of Keras for a moneygrab, practically on par with that PyImageSearch dude. Counterpoint: the Goodfellow DL book is a regular ole textbook; Maybe the point of this is that it abstracts most of the details and gives a higher level overview that's targeted at laymen? You're trying to sell your textbook by telling people that it's a gateway for them to become DL experts and get hired and make bank? That is a massive overreach, to claim that Keras is THE DL library.


Top 10 Amazon Books in Artificial Intelligence & Machine Learning, 2016 Edition

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This post details the 10 most popular titles in Amazon's Artificial Intelligence & Machine Learning Books category as of Nov 24, 2016, skipping over repeated titles as well as titles which have been obviously miscategorized and are of no use to our readers. He describes the basics of machine learning and some applications; the use of machine learning algorithms for pattern recognition; artificial neural networks inspired by the human brain; algorithms that learn associations between instances, with such applications as customer segmentation and learning recommendations; and reinforcement learning, when an autonomous agent learns act so as to maximize reward and minimize penalty. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning.


Markov Models: Understanding Markov Models and Unsupervised Machine Learning in Python with Real-World Applications

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Would you like to unlock the mysteries of Data Science? It's never been easier to make predictions and smart analysis with the use of Markov Models. And if you're unfamiliar with Python programming or Machine learning, don't worry, it'll all be explained in this book. You'll discover how to solve almost-unsolvable machine learning problems in no time.


Top April Stories: 10 Free Must-Read Books for Machine Learning and Data Science

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Most Viewed and Most Shared - Platinum Badge ( 20,000 UPV AND 2,000 shares) 10 Free Must-Read Books for Machine Learning and Data Science, by Matthew Mayo Most Viewed - Gold Badges ( 10,000 UPV) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Most Viewed - Silver Badges ( 5,000 unique PV) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm, by Jahnavi Mahanta Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) New Online Data Science Tracks for 2017, by Brendan Martin (new) Cartoon: Machine Learning - What They Think I Do, by Harrison Kinsley Data Science for the Layman (No Math Added), Annalyn Ng and Kenneth Soo Most Shared - Gold Badges ( 1,000 shares) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions, by Gregory Piatetsky Top 20 Recent Research Papers on Machine Learning and Deep Learning, by Thuy Pham Top mistakes data scientists make when dealing with business people, by Karolis Urbonas (new) Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um Most Shared - Silver Gold Badges ( 500 shares) Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Data Science for the Layman (No Math Added) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new) Cartoon: Machine Learning - What They Think I Do Awesome Deep Learning: Most Cited Deep Learning Papers, by Terry Taewoong Um 5 Machine Learning Projects You Can No Longer Overlook, April, by Matthew Mayo Keep it simple! How to understand Gradient Descent algorithm A Brief History of Artificial Intelligence, By Francesco Corea The 42 V's of Big Data and Data Science, by Tom Shafer (new) Data Science for the Layman (No Math Added) Deep Stubborn Networks - A Breakthrough Advance Towards Adversarial Machine Intelligence, by Matthew Mayo (new) Cartoon: Machine Learning - What They Think I Do


are-you-a-pronoun-or-an-adverb-a-sideways-glance-at-language-and-its-behaviour

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Representing the pronoun we have Jacque Derrida and his creation Deconstructionism, our adverb is the thinker Ludwig Wittgenstein and the idea of'Language games'. Moreover, one sees this core value, or use of the pronoun – as being a very suitable metaphor for the Post-structuralist French philosopher Jacque Derrida's work. In his book On Grammatology, Derrida writes, 'Descartes's analyticism is intuitionist, that of Leibniz points beyond mani-fest evidence, toward order, relation, point of view' [5]. Especially when faced with another fact, We humans are the things that create meaning – meaning is not derived from the things we have created.


5 Free Statistics eBooks You Need to Read This Autumn

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You'll work with a case study throughout the book to help you learn the entire data analysis process – from collecting data and generating statistics to identifying patterns and testing hypotheses. This book covers the essential exploratory techniques for summarizing data with R. Exploratory data analysis is a key part of the data science process because it allows you to sharpen your question and refine your modelling strategies to develop more complex statistical models. Some of the topics covered are making exploratory graphs, principles of analytic graphics, plotting systems and graphics devices in R, clustering methods, and dimension reduction techniques. Topics covered include probability, random variables, expectations, variability, distributions, limits and confidence intervals, testing, p-values, power, Bootstrapping and permutation tests.


5 Free Data Science eBooks For Your Summer Reading List

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You will need a basic understanding of statistical concepts and R programming, and the book is intended for practicing Data Scientists but as long as you tick these boxes you should be fine. The book is offered on the Pay-What-You-Want model, including free, and helpfully, they also offer it as a tablet-friendly pdf, also free. Instead of explaining the mathematics and theory, and then showing examples, the authors start with a practical data-related life science challenge. There is also a free Microsoft Excel Practical Data Cleaning template to help you get a good start with your data.