Python

The legendary comedian, actor, and writer of Monty Python, Fawlty Towers, and A Fish Called Wanda fame shares his key ideas about creativity: that it’s a learnable, improvable skill.“Many people have written about creativity, but although they were very, very clever, they weren't actually creative. I like to think I'm writing about it from the inside.”—John Cleese You might think that creativity is some mysterious, rare gift—one that only a few possess. But you’d be wrong. As John Cleese shows in this short, practical, and often amusing guide, creativity is a skill that anyone can acquire. Drawing on his lifelong experience as a writer, Cleese shares his insights into the nature of creativity and offers advice on how to get your own inventive juices flowing. What do you need to do to get yourself in the right frame of mind? When do you know that you’ve come up with an idea that might be worth pursuing? What should you do if you think you’ve hit a brick wall? We can all

This book introduces the field of data science in a practical and accessible manner, using a hands-on approach that assumes no prior knowledge of the subject. The foundational ideas and techniques of data science are provided independently from technology, allowing students to easily develop a firm understanding of the subject without a strong technical background, as well as being presented with material that will have continual relevance even after tools and technologies change. Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data. A suite of online material for both instructors and students provides a strong supplement to the book, including datasets, chapter slides, solutions, sample exams and curriculum suggestions. This entry-level textbook is ideally suited to readers from a range of disciplines wishing to build a practical, working knowledge of data science.

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

This third edition provides a concise yet approachable introduction to seismic theory, designed as a first course for graduate students or advanced undergraduate students. It clearly explains the fundamental concepts, emphasizing intuitive understanding over lengthy derivations, and outlines the different types of seismic waves and how they can be used to resolve Earth structure and understand earthquakes. New material and updates have been added throughout, including ambient noise methods, shear-wave splitting, back-projection, migration and velocity analysis in reflection seismology, earthquake rupture directivity, and fault weakening mechanisms. A wealth of both reworked and new examples, review questions and computer-based exercises in MATLAB®/Python give students the opportunity to apply the techniques they have learned to compute results of interest and to illustrate Earth's seismic properties. More advanced sections, which are not needed to understand the other material, are fla

This third edition provides a concise yet approachable introduction to seismic theory, designed as a first course for graduate students or advanced undergraduate students. It clearly explains the fundamental concepts, emphasizing intuitive understanding over lengthy derivations, and outlines the different types of seismic waves and how they can be used to resolve Earth structure and understand earthquakes. New material and updates have been added throughout, including ambient noise methods, shear-wave splitting, back-projection, migration and velocity analysis in reflection seismology, earthquake rupture directivity, and fault weakening mechanisms. A wealth of both reworked and new examples, review questions and computer-based exercises in MATLAB®/Python give students the opportunity to apply the techniques they have learned to compute results of interest and to illustrate Earth's seismic properties. More advanced sections, which are not needed to understand the other material, are fla

This unique text provides a thorough, yet accessible, grounding in the mathematics, statistics, and programming that students need to master for coursework and research in climate science, meteorology, and oceanography. Assuming only high school mathematics, it presents carefully selected concepts and techniques in linear algebra, statistics, computing, calculus and differential equations within the context of real climate science examples. Computational techniques are integrated to demonstrate how to visualize, analyze, and apply climate data, with R code featured in the book and both R and Python code available online. Exercises are provided at the end of each chapter with selected solutions available to students to aid self-study and further solutions provided online for instructors only. Additional online supplements to aid classroom teaching include datasets, images, and animations. Guidance is provided on how the book can support a variety of courses at different levels, making i

The Earth and environmental sciences are becoming progressively more quantitative due to the increased use of mathematical models and new data analysis techniques. This accessible introduction presents an overview of the mathematical methods essential for understanding Earth processes, providing an invaluable resource for students and early career researchers who may have missed (or forgotten) the mathematics they need to succeed as scientists. Topics build gently from basic methods such as calculus to more advanced techniques including linear algebra and differential equations. The practical applications of the mathematical methods to a variety of topics are discussed, ranging from atmospheric science and oceanography to biogeochemistry and geophysics. Including over 530 exercises and end-of-chapter problems, as well as additional computer codes in Python and MATLAB®, this book supports readers in applying appropriate analytical or computational methods to solving real research questi

A complete resource for finance students, this textbook presents the most common empirical approaches in finance in a comprehensive and well-illustrated manner that shows how econometrics is used in practice, and includes detailed case studies to explain how the techniques are used in relevant financial contexts. Maintaining the accessible prose and clear examples of previous editions, the new edition of this best-selling textbook provides support for the main industry-standard software packages, expands the coverage of introductory mathematical and statistical techniques into two chapters for students without prior econometrics knowledge, and includes a new chapter on advanced methods. Learning outcomes, key concepts and end-of-chapter review questions (with full solutions online) highlight the main chapter takeaways and allow students to self-assess their understanding. Online resources include extensive teacher and student support materials, including EViews, Stata, R, and Python so

Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Pytho

Renowned for their courage, their chivalry, and their ability to fight mythical creatures, the Knights of the Round Table must face their most dangerous opponents yet—DINOSAURS!This heavily illustrated middle grade novel from award-winning author and artist Matt Phelan is a hilarious, rip-roaring tale of derring-do perfect for reluctant readers and fans of How to Train Your Dragon and The Terrible Two. Let me tell you a secret about the Knights of the Round Table: they don’t have much to do. The realm is at peace and dragons are few and far between. So Merlin decides to send the knights out on a real adventure to a world filled with the most terrible lizards of all: DINOSAURS! Knights vs. Dinosaurs is a highly illustrated, fast-paced adventure full of uproarious knightly hijinks, surprising secrets, and terrifying dinosaurs. With art on nearly every page, including an epic fight scene depicted in several graphic-novel style spreads, this engaging story is Monty Python for young middle

Optical interferometry is a powerful technique to make images on angular scales hundreds of times smaller than is possible with the largest telescopes. This concise guide provides an introduction to the technique for graduate students and researchers who want to make interferometric observations and acts as a reference for technologists building new instruments. Starting from the principles of interference, the author covers the core concepts of interferometry, showing how the effects of the Earth's atmosphere can be overcome using closure phase, and the complete process of making an observation, from planning to image reconstruction. This rigorous approach emphasizes the use of rules-of-thumb for important parameters such as the signal-to-noise ratios, requirements for sampling the Fourier plane and predicting image quality. The handbook is supported by web resources, including the Python source code used to make many of the graphs, as well as an interferometry simulation framework, a

Non-literal language is ubiquitous in everyday life, and while hyperbole is a major part of this, it has so far remained relatively unexplored. This volume provides the first investigation of hyperbole in English, drawing on data from genres such as spoken conversation, TV, newspapers, and literary works from Chaucer to Monty Python. Combining quantitative and qualitative analyses, it uses approaches from semantics, pragmatics, discourse analysis and classical rhetoric to investigate in detail both speaker-centered and emotive aspects of hyperbole, and also addressee-related aspects, such as interpretation and interactional uptake. Illustrated with a range of diachronic case studies, hyperbole is also shown to be a main means of linguistic creativity, and an important contributor to language change. The book concludes with an exploration of the role of hyperbole in political speaking, humour, and literature. Original and in-depth, it will be invaluable to all those working on meaning,

Non-literal language is ubiquitous in everyday life, and while hyperbole is a major part of this, it has so far remained relatively unexplored. This volume provides the first investigation of hyperbole in English, drawing on data from genres such as spoken conversation, TV, newspapers, and literary works from Chaucer to Monty Python. Combining quantitative and qualitative analyses, it uses approaches from semantics, pragmatics, discourse analysis and classical rhetoric to investigate in detail both speaker-centered and emotive aspects of hyperbole, and also addressee-related aspects, such as interpretation and interactional uptake. Illustrated with a range of diachronic case studies, hyperbole is also shown to be a main means of linguistic creativity, and an important contributor to language change. The book concludes with an exploration of the role of hyperbole in political speaking, humour, and literature. Original and in-depth, it will be invaluable to all those working on meaning,

Molecular simulation is a powerful tool in materials science, physics, chemistry and biomolecular fields. This updated edition provides a pragmatic introduction to a wide range of techniques for the simulation of molecular systems at the atomic level. The first part concentrates on methods for calculating the potential energy of a molecular system, with new chapters on quantum chemical, molecular mechanical and hybrid potential techniques. The second part describes methods examining conformational, dynamical and thermodynamical properties of systems, covering techniques including geometry-optimization, normal-mode analysis, molecular dynamics, and Monte Carlo simulation. Using Python, the second edition includes numerous examples and program modules for each simulation technique, allowing the reader to perform the calculations and appreciate the inherent difficulties involved in each. This is a valuable resource for researchers and graduate students wanting to know how to use atomic-sc

The simulation of physical systems requires a simplified, hierarchical approach which models each level from the atomistic to the macroscopic scale. From quantum mechanics to fluid dynamics, this book systematically treats the broad scope of computer modeling and simulations, describing the fundamental theory behind each level of approximation. Berendsen evaluates each stage in relation to its applications giving the reader insight into the possibilities and limitations of the models. Practical guidance for applications and sample programs in Python are provided. With a strong emphasis on molecular models in chemistry and biochemistry, this 2007 book will be suitable for advanced undergraduate and graduate courses on molecular modeling and simulation within physics, biophysics, physical chemistry and materials science. It will also be a useful reference to all those working in the field. Additional resources for this title including solutions for instructors and programs are available