Python is easy to learn, and it's perfect for exploring topics like statistics, geometry, probability, and calculus. You’ll learn to write programs to find derivatives, solve equations graphically, manipulate algebraic expressions, even examine projectile motion.Rather than crank through tedious calculations by hand, you'll learn how to use Python functions and modules to handle the number crunching while you focus on the principles behind the math. Exercises throughout teach fundamental programming concepts, like using functions, handling user input, and reading and manipulating data. As you learn to think computationally, you'll discover new ways to explore and think about math, and gain valuable programming skills that you can use to continue your study of math and computer science.If you’re interested in math but have yet to dip into programming, you’ll find that Python makes it easy to go deeper into the subject—let Python handle the tedious work while you spend more time on the math.

    It has become adevelopment platform in it own right. Applications written using Excel are partof many corporations' core suites of business-critical applications. In spite ofthis, Excel is too often thought of as a hobbyist's platform. While there arenumerous titles on Excel and VBA, until now there have been none thatprovide an overall explanation of how to develop professional-quality Excel-basedapplications. All three authors are professional Excel developers who runtheir own companies developing Excel-based apps for clients ranging fromindividuals to the largest multinational corporations. In this book they showhow anyone from power users to professional developers can increase thespeed and usefulness of their Excel-based apps.

    It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of "data science" for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (or MOOC) offered by the pioneering MIT--Harvard collaboration edX.Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. The book does not require knowledge of mathematics beyond high school algebra, but does assume that readers are comfortable with rigorous thinking and not intimidated by mathematical concepts. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming.Introduction to Computation and Programming Using Python can serve as a stepping-stone to more advanced computer science courses, or as a basic grounding in computational problem solving for students in other disciplines.

    It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more.In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data.

    The book teaches a range of design and analysis techniques for problems that arise in computing applications. The text encourages an understanding of the algorithm design process and an appreciation of the role of algorithms in the broader field of computer science.

    As a professional C++ developer and former Harvard teaching fellow, I know what you need to know to be a great C++ programmer, and I know how to teach it, one step at a time. I know where people struggle, and why, and how to make it clear. I cover every step of the programming process, including:Getting the tools you need to program and how to use them*Basic language feature like variables, loops and functions*How to go from an idea to code*A clear, understandable explanation of pointers*Strings, file IO, arrays, references*Classes and advanced class design*C++-specific programming patterns*Object oriented programming*Data structures and the standard template library (STL)Key concepts are reinforced with quizzes and over 75 practice problems.

    This bestselling Sybex study guide covers 100% of all exam objectives. You'll prepare for the exam smarter and faster with Sybex thanks to expert content, real-world examples, advice on passing each section of the exam, access to the Sybex online interactive learning environment, and much more. Reinforce what you've learned with key topic exam essentials and chapter review questions. Along with the book, you also get access to Sybex's superior online interactive learning environment that includes: Four unique 250 question practice exams to help you identify where you need to study more. Get more than 90 percent of the answers correct, and you're ready to take the certification exam. More than 650 Electronic Flashcards to reinforce your learning and give you last-minute test prep before the exam A searchable glossary in PDF to give you instant access to the key terms you need to know for the exam Coverage of all of the exam topics in the book means you'll be ready for: Security and Risk Management Asset Security Security Engineering Communication and Network Security Identity and Access Management Security Assessment and Testing Security Operations Software Development Security