Renowned tech investor and start-up guru Brad Feld lights YOU on fire with this insider's book that will teach you how to launch, fund and run your own company. If you're already an entrepreneur or have always dreamed of being one, douse yourself in “Feld Thoughts” and catch the spark. You'll be burning, entrepreneur, with this e-book!. Brad's blog is a backstage pass to the 24/7 rock show that is tech startups. It is a master class in startup investing for givers and takers of funds. It is a rolling critique of tech products vast and simple (with enough edge to make the most scathing restaurant critic in Manhattan blush). And it is the journal of a peripatetic marathoner who still believes he will crack the four-hour mark someday. From such Feld Thoughts, we have constructed “The Burning Entrepreneur,” the e-book on startups that you would take to a desert island if it had electricity, a decent Internet connection and angel investors. “The Burning Entrepreneur” illuminates the actions and attitudes required to launch, fund and ignite your startup. Brad Feld is on fire. Find out what happens when you stand too close. TABLE OF CONTENTS - Introduction - Be on Fire - Be In Love With Your Business - Don't Be in the 99% - Ignorance Is Success - Meet the New Boss, NOT the Same as the Old Boss - Hire the Right People - It's the Product, Stupid! - Learning to Program: A Case Study - I Don't Hate Marketing: Neither Should You - OK, It's Really the Money, Stupid - Keeping Your You-Know-What Together - Burning Examples - Conclusion - Recommended Books for Burning Entrepreneurs GREAT EXCERPTS FROM THE BOOK It was during that car ride that my dad hit me with words that would prove to be fundamental for me: “If you aren’t standing on the edge you are taking up too much space.” Thirty years later that line continues to be a defining characteristic for how I live my life. I’m constantly pushing, looking for the edge of whatever I do. (pg 14) Over time, I’ve learned that none of the short-term moves in the stock market matter at all in my life. It’s occasionally entertaining to turn on CNBC and see my friend Paul Kedrosky in the octobox telling all the other people that they don’t actually understand macro-economics, but it’s no different than watching McEnroe when he’s announcing a Nadal–Federer match. It’s just sport. (pg 33) I don’t create products anymore (I invest in companies that create them), but I’m a great alpha tester. I’ve always been good at this for some reason — bugs just find me. While my UX design skills are merely adequate, I’ve got a great feel for how to simplify things and make them cleaner. (pg 58) If you are someone who spends 30 minutes or more a day “organizing yourself,” I encourage you to step back and think about what you could change and how that might shift you from focusing on organizing to working toward outcomes. It’s liberating. (pg 103) ...buy a copy to read more!

    Right? Wrong, says Alex Rosenberg in How History Gets Things Wrong. Feeling especially well-informed after reading a book of popular history on the best-seller list? Don't. Narrative history is always, always wrong. It's not just incomplete or inaccurate but deeply wrong, as wrong as Ptolemaic astronomy. We no longer believe that the earth is the center of the universe. Why do we still believe in historical narrative? Our attachment to history as a vehicle for understanding has a long Darwinian pedigree and a genetic basis. Our love of stories is hard-wired. Neuroscience reveals that human evolution shaped a tool useful for survival into a defective theory of human nature.Stories historians tell, Rosenberg continues, are not only wrong but harmful. Israel and Palestine, for example, have dueling narratives of dispossession that prevent one side from compromising with the other. Henry Kissinger applied lessons drawn from the Congress of Vienna to American foreign policy with disastrous results. Human evolution improved primate mind reading—the ability to anticipate the behavior of others, whether predators, prey, or cooperators—to get us to the top of the African food chain. Now, however, this hard-wired capacity makes us think we can understand history—what the Kaiser was thinking in 1914, why Hitler declared war on the United States—by uncovering the narratives of what happened and why. In fact, Rosenberg argues, we will only understand history if we don't make it into a story.

    For all of neuroscience's advances, we've made little progress on its biggest question: How do simple cells in the brain create intelligence? Jeff Hawkins and his team discovered that the brain uses maplike structures to build a model of the world-not just one model, but hundreds of thousands of models of everything we know. This discovery allows Hawkins to answer important questions about how we perceive the world, why we have a sense of self, and the origin of high-level thought.

    

    This simple way to manage your household income and expenses includes a stylish cover, coin purse, places for your checkbook and check register, memo pad, debit card holders, and extra cash-management envelopes.

    Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either."

    To become an effective leader in whatever leadership role or capacity, there are leadership qualities or leadership characteristics you need to lead effectively. Communication skills and negotiating skills may be just some of the qualities of a good leader. In this book, the author shares some of the most powerful insights that will help you to become a visionary and inspiring leader in whatever spectrum of leadership. Leadership Skills: Essentials of Leadership and the Skills Required to Lead Effectively Tags: leadership skills, leadership, lead, leader, leaders, leading, effective leadership, leadership qualities, leadership characteristics, business leadership, women in leadership, john Maxwell, creativity, decision making, making ideas happen, leadership styles, inspiring people, inspiring leaders, leadership advice, leadership development, leadership training, good leadership skills, leadership quotes, leadership definition, effective leadership skills, good leadership qualities, inspiring action, women's leadership, on leadership, situational leadership, leadership books, best leadership books, books on leadership, qualities of a good leader, qualities of a leader, team leader skills, managerial skills, communication skills, team leadership, leadership traits, visionary leadership, leadership academy, transactional leadership, authentic leadership, educational leadership, adaptive leadership, leadership vs management, time management

    Oliver Sacks--called "the poet laureate of medicine" by the New York Times--illuminated the mysteries of the brain for a wide audience in a series of richly acclaimed books, including Awakenings and The Man Who Mistook His Wife for a Hat, and numerous The New Yorker articles. In this collection of interviews, Sacks is at his most candid and disarming, rich with insights about his life and work. Any reader of Oliver Sacks will find in this book an entirely new way of looking at a brilliant writer"--

    These topics lie at the heart of many exciting areas of contemporary science and engineering - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics, and cryptography. This textbook introduces theory in tandem with applications. Information theory is taught alongside practical communication systems, such as arithmetic coding for data compression and sparse-graph codes for error-correction. A toolbox of inference techniques, including message-passing algorithms, Monte Carlo methods, and variational approximations, are developed alongside applications of these tools to clustering, convolutional codes, independent component analysis, and neural networks. The final part of the book describes the state of the art in error-correcting codes, including low-density parity-check codes, turbo codes, and digital fountain codes -- the twenty-first century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, David MacKay's groundbreaking book is ideal for self-learning and for undergraduate or graduate courses. Interludes on crosswords, evolution, and sex provide entertainment along the way. In sum, this is a textbook on information, communication, and coding for a new generation of students, and an unparalleled entry point into these subjects for professionals in areas as diverse as computational biology, financial engineering, and machine learning.

    Graphs are not included, but both the graphs and the concepts behind them are explained; only basic math is included, and you can even skim over the math and still come away with an understanding of the concepts; statistics is not included at all.BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is an easy way to learn concepts relating to economics and the economy. It is a product of thousands of hours spent online, teaching basic concepts in economics to hundreds of students worldwide over the course of the past several years. From back and forth communications, I have discovered the explanations for the concepts that students find easiest to understand, as well as the areas that most often get misunderstood and under-emphasized.I have worked with students located throughout the United States and from many different countries, on six different continents; students from many different school systems with different points of emphasis; students with different levels of knowledge, different backgrounds, and different levels of interest in the subject. I have received numerous comments and testimonials regarding the teaching methods that I incorporate in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE.The subject matter included in BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE comes from a compilation of many different textbooks at the introductory and intermediate levels. My goal was to include every subject in economics that normally will be found in an introductory level textbook of economics, microeconomics, or macroeconomics. Since different school systems, different classroom instructors, and different textbooks cover a slightly different combination of topics, BASIC ECONOMICS FOR STUDENTS AND NON-STUDENTS ALIKE is a little more comprehensive than most single introductory textbooks of economics. Some of the topics will be found in introductory classes in some schools, but in intermediate-level classes in other schools.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    Great leaders are charismatic and articulate, and they use these attributes to rally the troops.The following is a collection of the best motivational speeches, inspirational words, sure to motivate you when you need it most by the greatest leaders of our time.Authors: Bill Gates, Rick Rigsby, Denzel Washington, Jim Carrey, J. K. Rowling, Matthew McConaughey, Steve Jobs, Admiral William H. McRaven, Tony RobbinsNarrators: Bill Gates, Rick Rigsby, Denzel Washington, Jim Carrey, J. K. Rowling, Matthew McConaughey, Steve Jobs, Admiral William H. McRaven, Tony RobbinsENGLISH (UNABRIDGED)3H 57M

    However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background. Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice-freeing you to get results using computing power. Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention. Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback. You'll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you've mastered these techniques, you'll constantly turn to this guide for the working PyMC code you need to jumpstart future projects. Coverage includes - Learning the Bayesian "state of mind" and its practical implications - Understanding how computers perform Bayesian inference - Using the PyMC Python library to program Bayesian analyses - Building and debugging models with PyMC - Testing your model's "goodness of fit" - Opening the "black box" of the Markov Chain Monte Carlo algorithm to see how and why it works - Leveraging the power of the "Law of Large Numbers" - Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning - Using loss functions to measure an estimate's weaknesses based on your goals and desired outcomes - Selecting appropriate priors and understanding how their influence changes with dataset size - Overcoming the "exploration versus exploitation" dilemma: deciding when "pretty good" is good enough - Using Bayesian inference to improve A/B testing - Solving data science problems when only small amounts of data are available Cameron Davidson-Pilon has worked in many areas of applied mathematics, from the evolutionary dynamics of genes and diseases to stochastic modeling of financial prices. His contributions to the open source community include lifelines, an implementation of survival analysis in Python. Educated at the University of Waterloo and at the Independent University of Moscow, he currently works with the online commerce leader Shopify.

    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.

    Acclaimed technologist and inventor Amir Husain explains how we can live amidst the coming age of sentient machines and artificial intelligence—and not only survive, but thrive.Artificial “machine” intelligence is playing an ever-greater role in our society. We are already using cruise control in our cars, automatic checkout at the drugstore, and are unable to live without our smartphones. The discussion around AI is polarized; people think either machines will solve all problems for everyone, or they will lead us down a dark, dystopian path into total human irrelevance. Regardless of what you believe, the idea that we might bring forth intelligent creation can be intrinsically frightening. But what if our greatest role as humans so far is that of creators? Amir Husain, a brilliant inventor and computer scientist, argues that we are on the cusp of writing our next, and greatest, creation myth. It is the dawn of a new form of intellectual diversity, one that we need to embrace in order to advance the state of the art in many critical fields, including security, resource management, finance, and energy. “In The Sentient Machine, Husain prepares us for a brighter future; not with hyperbole about right and wrong, but with serious arguments about risk and potential” (Dr. Greg Hyslop, Chief Technology Officer, The Boeing Company). He addresses broad existential questions surrounding the coming of AI: Why are we valuable? What can we create in this world? How are we intelligent? What constitutes progress for us? And how might we fail to progress? Husain boils down complex computer science and AI concepts into clear, plainspoken language and draws from a wide variety of cultural and historical references to illustrate his points. Ultimately, Husain challenges many of our societal norms and upends assumptions we hold about “the good life.”