Content Machine

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.

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.

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.

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

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?

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.

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

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).