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Deep Learning
by Ian Goodfellow, Yoshua Bengio, Aaron Courville

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.

“Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.”
—Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.

The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.

Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.


An Introduction to Statistical Learning
by Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.

Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.


The Urantia Book
by Urantia Foundation

You have just discovered the literary masterpiece that answers your questions about God, life in the inhabited universe, the history and future of this world, and the life of Jesus.  The Urantia Book harmonizes history, science, and religion into a philosophy of living that brings new meaning and hope into your life. If you are searching for answers, read The Urantia Book!

The world needs new spiritual truth that provides modern men and women with an intellectual pathway into a personal relationship with God. Building on the world’s religious heritage, The Urantia Book describes an endless destiny for humankind, teaching that living faith is the key to personal spiritual progress and eternal survival.  These teachings provide new truths powerful enough to uplift and advance human thinking and believing for the next 1000 years.

A third of The Urantia Book is the inspiring story of Jesus’ entire life and a revelation of his original teachings. This panoramic narrative includes his birth, childhood, teenage years, adult travels and adventures, public ministry, crucifixion, and 19 resurrection appearances.  This inspiring story recasts Jesus from the leading figure of Christianity into the guide for seekers of all faiths and all walks of life.


Adobe Acrobat 9 PDF Bible
by Ted Padova

Find just what you need to incorporate PDFs in your workflows with the newest edition of this perennial top-selling reference and tutorial from Acrobat guru, Ted Padova. He packs these pages with tips guaranteed to help you get the most out of this powerful software. You’ll find techniques for creating, editing, and repurposing PDFs for everything from print to CD-ROMs, the Internet, e-book content, and more. Keep this classic within reach—you’ll turn to it again and again.

Note: CD-ROM/DVD and other supplementary materials are not included as part of eBook file.


The Law of One
by L-L Research Staff


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