Use applied math to map fire stations, develop facial recognition software, solve the art gallery problem and more in this hands-on, real-world infosec book.Explore the intersection of mathematics and computer security with this engaging and accessible guide.Math for Security will equip you with essential tools to tackle complex security problems head on. All you need are some basic programming skills. Once you’ve set up your development environmen...

The classic guide to how computers work, updated with new chapters and interactive graphics"For me, Code was a revelation. It was the first book about programming that spoke to me. It started with a story, and it built up, layer by layer, analogy by analogy, until I understood not just the Code, but the System. Code is a book that is as much about Systems Thinking and abstractions as it is about code and programming. Code teaches us how many unseen layers there...

Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library.Transformers have been used to write realistic news stories, improve Google Search queries, and ...

R is the world's most popular language for developing statistical software: Archaeologists use it to track the spread of ancient civilizations, drug companies use it to discover which medications are safe and effective, and actuaries use it to assess financial risks and keep economies running smoothly.The Art of R Programming takes you on a guided tour of software development with R, from basic types and data structures to advanced topics like closures, recursion, and anon...

Python is a first-class tool for many researchers, primarily because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the new edition of Python Data Science Handbook do you get them all--IPython, NumPy, pandas, Matplotlib, scikit-learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find the seco...

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects.If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own m...

This is a tutorial-driven and practical, but well-grounded book showcasing good Machine Learning practices. There will be an emphasis on using existing technologies instead of showing how to write your own implementations of algorithms. This book is a scenario-based, example-driven tutorial. By the end of the book you will have learnt critical aspects of Machine Learning Python projects and experienced the power of ML-based systems by actually working on them.This book primarily targets Py...

Unleash the power of computer vision with Python using OpenCV Key FeaturesBook DescriptionOpenCV 3 is a state-of-the-art computer vision library that allows a great variety of image and video processing operations. Some of the more spectacular and futuristic features such as face recognition or object tracking are easily achievable with OpenCV 3. Learning the basic concepts behind computer vision algorithms, models, and OpenCV's API will enable the dev...

Learn to solve challenging data science problems by building powerful machine learning models using PythonAbout This BookUnderstand which algorithms to use in a given context with the help of this exciting recipe-based guideThis practical tutorial tackles real-world computing problems through a rigorous and effective approachBuild state-of-the-art models and develop personalized recommendations to perform machine learning at scal...

A primer on the underlying technologies that allow computer programs to work. Covers topics like computer hardware, combinatorial logic, sequential logic, computer architecture, computer anatomy, and Input/Output.Many coders are unfamiliar with the underlying technologies that make their programs run. But why should you care when your code appears to work? Because you want it to run well and not be riddled with hard-to-find bugs. You don't want to be in the news be...

Unleash the power of Python and its robust data science capabilitiesKey FeaturesUnleash the power of Python 3 objectsLearn to use powerful Python libraries for effective data processing and analysisHarness the power of Python to analyze data and create insightful predictive modelsUnlock deeper insights into machine learning with this vital guide to cutting-edge predictive analyticsBook DescriptionThe Python: Real-Wor...

Many programmers would love to use Perl for projects that involve heavy lifting, but miss the many traditional algorithms that textbooks teach for other languages. Computer scientists have identified many techniques that a wide range of programs need, such as:Fuzzy pattern matching for text (identify misspellings!)Finding correlations in dataGame-playing algorithmsPredicting phenomena such as Web trafficPolynomial and spline fitting