Content Machine Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Content Machine book. This book definitely worth reading, it is an incredibly well-written.
This ground-breaking study, the first of its kind, outlines a theory of publishing that allows publishing houses to focus on their core competencies in times of crisis. Tracing the history of publishing from the press works of fifteenth-century Germany to twenty-first-century Silicon Valley, via Venice, Beijing, Paris and London, and fusing media theory and business experience, ‘The Content Machine’ offers a new understanding of content, publishing and technology, and defiantly answers those who contend that publishing has no future in a digital age.
Content Machine outlines a strategy for using content marketing to build a 7-figure business with zero advertising. Whether you are a blogger, content marketer, entrepreneur or marketing manager, Content Machine will help you see the return you deserve from your content marketing efforts.
Like many content creators today, I’ve faced the same problems that plague any industry that’s surging in demand. Losing sleep on projects, taking on too many clients, burning out, you know the drill. There’s a price for success in the content creation industry if you’re doing it all on your own. My name is Vanessa Lau and I’ve spent the past four years learning, tweaking, and refining the perfect system of how you can deliver exceptional thought leadership content sustainability and consistently. This is what I call the content machine.
Your new product is ready to launch and you’re itching to tell potential customers all about it. But how do you make your message stand out above all the noise and marketing clutter? Take the guesswork out of content management with this hands-on guide. You’ll learn how to produce and manage powerful content pieces that speak directly to customers and compel them to respond. Author Laura Busche walks you through content strategies and tactics drawn from business, design, and psychology insights. Packed with examples and exercises, this book teaches you how to tell your story with engaging copy, potent images, and striking design—all carefully orchestrated through well-oiled production management. Solopreneurs, startups, marketing managers, and execs will learn 10 Essential Steps to Content Success, with deep dives into: Content strategy: understand your audience, choose and prioritize channels, and find your brand’s core themes, voice, and tone Content creation: craft an engaging experience with content formats and copywriting formulas and templates Content management: organize, delegate, and supervise tasks; optimize the content production process to reuse successful patterns
Machine Learning Pocket Reference by Matt Harrison Pdf
With detailed notes, tables, and examples, this handy reference will help you navigate the basics of structured machine learning. Author Matt Harrison delivers a valuable guide that you can use for additional support during training and as a convenient resource when you dive into your next machine learning project. Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data. You’ll also learn methods for clustering, predicting a continuous value (regression), and reducing dimensionality, among other topics. This pocket reference includes sections that cover: Classification, using the Titanic dataset Cleaning data and dealing with missing data Exploratory data analysis Common preprocessing steps using sample data Selecting features useful to the model Model selection Metrics and classification evaluation Regression examples using k-nearest neighbor, decision trees, boosting, and more Metrics for regression evaluation Clustering Dimensionality reduction Scikit-learn pipelines
Probabilistic Machine Learning by Kevin P. Murphy Pdf
A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.
A brilliant probe into the political and psychological effects of our changing relationship with social media Former social media executives tell us that the system is an addiction-machine. We are users, waiting for our next hit as we like, comment and share. We write to the machine as individuals, but it responds by aggregating our fantasies, desires and frailties into data, and returning them to us as a commodity experience. The Twittering Machine is an unflinching view into the calamities of digital life: the circus of online trolling, flourishing alt-right subcultures, pervasive corporate surveillance, and the virtual data mines of Facebook and Google where we spend considerable portions of our free time. In this polemical tour de force, Richard Seymour shows how the digital world is changing the ways we speak, write, and think. Through journalism, psychoanalytic reflection and insights from users, developers, security experts and others, Seymour probes the human side of the machine, asking what we’re getting out of it, and what we’re getting into. Social media held out the promise that we could make our own history–to what extent did we choose the nightmare that it has become?
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson,Andrew McAfee Pdf
A pair of technology experts describe how humans will have to keep pace with machines in order to become prosperous in the future and identify strategies and policies for business and individuals to use to combine digital processing power with human ingenuity.
An authority on creativity introduces us to AI-powered computers that are creating art, literature, and music that may well surpass the creations of humans. Today's computers are composing music that sounds “more Bach than Bach,” turning photographs into paintings in the style of Van Gogh's Starry Night, and even writing screenplays. But are computers truly creative—or are they merely tools to be used by musicians, artists, and writers? In this book, Arthur I. Miller takes us on a tour of creativity in the age of machines. Miller, an authority on creativity, identifies the key factors essential to the creative process, from “the need for introspection” to “the ability to discover the key problem.” He talks to people on the cutting edge of artificial intelligence, encountering computers that mimic the brain and machines that have defeated champions in chess, Jeopardy!, and Go. In the central part of the book, Miller explores the riches of computer-created art, introducing us to artists and computer scientists who have, among much else, unleashed an artificial neural network to create a nightmarish, multi-eyed dog-cat; taught AI to imagine; developed a robot that paints; created algorithms for poetry; and produced the world's first computer-composed musical, Beyond the Fence, staged by Android Lloyd Webber and friends. But, Miller writes, in order to be truly creative, machines will need to step into the world. He probes the nature of consciousness and speaks to researchers trying to develop emotions and consciousness in computers. Miller argues that computers can already be as creative as humans—and someday will surpass us. But this is not a dystopian account; Miller celebrates the creative possibilities of artificial intelligence in art, music, and literature.
Machine Learning for Multimedia Content Analysis by Yihong Gong,Wei Xu Pdf
This volume introduces machine learning techniques that are particularly powerful and effective for modeling multimedia data and common tasks of multimedia content analysis. It systematically covers key machine learning techniques in an intuitive fashion and demonstrates their applications through case studies. Coverage includes examples of unsupervised learning, generative models and discriminative models. In addition, the book examines Maximum Margin Markov (M3) networks, which strive to combine the advantages of both the graphical models and Support Vector Machines (SVM).
God, Human, Animal, Machine by Meghan O'Gieblyn Pdf
A strikingly original exploration of what it might mean to be authentically human in the age of artificial intelligence, from the author of the critically-acclaimed Interior States. • "At times personal, at times philosophical, with a bracing mixture of openness and skepticism, it speaks thoughtfully and articulately to the most crucial issues awaiting our future." —Phillip Lopate “[A] truly fantastic book.”—Ezra Klein For most of human history the world was a magical and enchanted place ruled by forces beyond our understanding. The rise of science and Descartes's division of mind from world made materialism our ruling paradigm, in the process asking whether our own consciousness—i.e., souls—might be illusions. Now the inexorable rise of technology, with artificial intelligences that surpass our comprehension and control, and the spread of digital metaphors for self-understanding, the core questions of existence—identity, knowledge, the very nature and purpose of life itself—urgently require rethinking. Meghan O'Gieblyn tackles this challenge with philosophical rigor, intellectual reach, essayistic verve, refreshing originality, and an ironic sense of contradiction. She draws deeply and sometimes humorously from her own personal experience as a formerly religious believer still haunted by questions of faith, and she serves as the best possible guide to navigating the territory we are all entering.
The Hundred-page Machine Learning Book by Andriy Burkov Pdf
Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you a good feel of more advanced topics to pursue.
Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB® codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/
Learn to expertly apply a range of machine learning methods to real data with this practical guide. Packed with real datasets and practical examples, The Art of Machine Learning will help you develop an intuitive understanding of how and why ML methods work, without the need for advanced math. As you work through the book, you’ll learn how to implement a range of powerful ML techniques, starting with the k-Nearest Neighbors (k-NN) method and random forests, and moving on to gradient boosting, support vector machines (SVMs), neural networks, and more. With the aid of real datasets, you’ll delve into regression models through the use of a bike-sharing dataset, explore decision trees by leveraging New York City taxi data, and dissect parametric methods with baseball player stats. You’ll also find expert tips for avoiding common problems, like handling “dirty” or unbalanced data, and how to troubleshoot pitfalls. You’ll also explore: How to deal with large datasets and techniques for dimension reduction Details on how the Bias-Variance Trade-off plays out in specific ML methods Models based on linear relationships, including ridge and LASSO regression Real-world image and text classification and how to handle time series data Machine learning is an art that requires careful tuning and tweaking. With The Art of Machine Learning as your guide, you’ll master the underlying principles of ML that will empower you to effectively use these models, rather than simply provide a few stock actions with limited practical use. Requirements: A basic understanding of graphs and charts and familiarity with the R programming language
Machine Dreaming and Consciousness by J. F. Pagel,Philip Kirshtein Pdf
Machine Dreaming and Consciousness is the first book to discuss the questions raised by the advent of machine dreaming. Artificial intelligence (AI) systems meeting criteria of primary and self-reflexive consciousness are often utilized to extend the human interface, creating waking experiences that resemble the human dream. Surprisingly, AI systems also easily meet all human-based operational criteria for dreaming. These “dreams are far different from anthropomorphic dreaming, including such processes as fuzzy logic, liquid illogic, and integration instability, all processes that may be necessary in both biologic and artificial systems to extend creative capacity. Today, multi-linear AI systems are being built to resemble the structural framework of the human central nervous system. The creation of the biologic framework of dreaming (emotions, associative memories, and visual imagery) is well within our technical capacity. AI dreams potentially portend the further development of consciousness in these systems. This focus on AI dreaming raises even larger questions. In many ways, dreaming defines our humanity. What is humanly special about the states of dreaming? And what are we losing when we limit our focus to its technical and biologic structure, and extend the capacity for dreaming into our artificial creations? Machine Dreaming and Consciousness provides thorough discussion of these issues for neuroscientists and other researchers investigating consciousness and cognition. Addresses the function and role of dream-like processing in AI systems Describes the functions of dreaming in the creative process of both humans and machines Presents an alternative approach to the philosophy of machine consciousness Provides thorough discussion of machine dreaming and consciousness for neuroscientists and other researchers investigating consciousness and cognition