Geo.Engineerings

Michael Paluszek, Stephanie Thomas ... 252 pages - Publisher: Apress; (February, 2020) ... Language: English - ISBN-10: 1484251237 - ISBN-13: 978-1484251232.

Harness the power of MATLAB for deep-learning challenges. This book provides an introduction to deep learning and using MATLAB's deep-learning toolboxes. You’ll see how these toolboxes provide the complete set of functions needed to implement all aspects of deep learning. Along the way, you'll learn to model complex systems, including the stock market, natural language, and angles-only orbit determination. You’ll cover dynamics and control, and integrate deep-learning algorithms and approaches using MATLAB. You'll also apply deep learning to aircraft navigation using images.

Finally, you'll carry out classification of ballet pirouettes using an inertial measurement unit to experiment with MATLAB's hardware capabilities. What You Will Learn: Explore deep learning using MATLAB and compare it to algorithms + Write a deep learning function in MATLAB and train it with examples + Use MATLAB toolboxes related to deep learning + Implement tokamak disruption prediction.

Ben Stephenson ... 219 pages - Publisher: Springer; 2nd edition (July, 2019) ... Language: English - ISBN-10: 3030188728 - ISBN-13: 978-3030188726.

This student-friendly textbook encourages the development of programming skills through active practice by focusing on exercises that support hands-on learning. The Python Workbook provides a compendium of 186 exercises, spanning a variety of academic disciplines and everyday situations. Solutions to selected exercises are also provided, supported by brief annotations that explain the technique used to solve the problem, or highlight a specific point of Python syntax. This enhanced new edition has been thoroughly updated and expanded with additional exercises, along with concise introductions that outline the core concepts needed to solve them. The exercises and solutions require no prior background knowledge, beyond the material covered in a typical introductory Python programming course.

Features: Uses an accessible writing style and easy-to-follow structure; includes a mixture of classic exercises from the fields of computer science and mathematics, along with exercises that connect to other academic disciplines; presents the solutions to approximately half of the exercises; provides annotations alongside the solutions, which explain the approach taken to solve the problem and relevant aspects of Python syntax; offers a variety of exercises of different lengths and difficulties; contains exercises that encourage the development of programming skills using if statements, loops, basic functions, lists, dictionaries, files, and recursive functions. Undergraduate students enrolled in their first programming course and wishing to enhance their programming abilities will find the exercises and solutions provided in this book to be ideal for their needs.

Bertrand S. Clarke, Jennifer L. Clarke ... 656 pages - Publisher: Cambridge Univ. Press; (April, 2018) ... Language: English - ISBN-10: 1107028280 - ISBN-13: 978-1107028289.

All scientific disciplines prize predictive success. Conventional statistical analyses, however, treat prediction as secondary, instead focusing on modeling and hence estimation, testing, and detailed physical interpretation, tackling these tasks before the predictive adequacy of a model is established. This book outlines a fully predictive approach to statistical problems based on studying predictors; the approach does not require predictors correspond to a model although this important special case is included in the general approach. Throughout, the point is to examine predictive performance before considering conventional inference. These ideas are traced through five traditional subfields of statistics, helping readers to refocus and adopt a directly predictive outlook. The book also considers prediction via contemporary 'black box' techniques and emerging data types and methodologies where conventional modeling is so difficult that good prediction is the main criterion available for evaluating the performance of a statistical method. Well-documented open-source R code in a Github repository allows readers to replicate examples and apply techniques to other investigations.

Bradley Efron, Trevor Hastie ... 495 pages - Publisher: Cambridge Univ. Press; (July, 2016) ... Language: English - ISBN-10: 1107149894 - ISBN-13: 978-1107149892.

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Valentina Emilia Balas, Sanjiban Sekhar Roy, Dharmendra Sharma, Pijush Samui ... 389 pages - Publisher: Springer; (March, 2019) ... Language: English - ISBN-10: 3030114783 - ISBN-13: 978-303011478.

This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Ryosuke Kitamura, Kazunari Sako ... 188 pages - Publisher: CRC Press; (August, 2019) ... Language: English - AmazonSIN: B07W86B2PY.

Soil is fundamentally a multi-phase material – consisting of solid particles, water and air. In soil mechanics and geotechnical engineering it is widely treated as an elastic, elastoplastic or visco-elastoplastic material, and consequently regarded as a continuum body. However, this book explores an alternative approach, considering soil as a multi-phase and discrete material and applying basic Newtonian mechanics rather than analytical mechanics. It applies microscopic models to the solid phase and fluid phases, and then introduces probability theory and statistics to derive average physical quantities which correspond to the soil‘s macroscopic physical properties such as void ratio and water content. This book is particularly focused on the mechanical behaviour of dry, partially saturated and full saturated sandy soil, as much of the physicochemical microscopic characteristic of clayey soil is still not clear. It explores the inter-particle forces at the point of contact of soil particles and the resultant inter-particle stresses, instead of the total stress and effective stress which are studied in mainstream soil mechanics. Deformation and strength behaviour, soil-water characteristic curves, and permeability coefficients of water and air are then derived simply from grain size distribution, soil particle density, void ratio and water content. A useful reference for consultants, professional engineers, researchers and public sector organisations involved in unsaturated soil tests. Advanced undergraduate and postgraduate students on Unsaturated Soil Mechanics courses will also find it a valuable text to study.