Warning: Trying to access array offset on false in E:\WWW\html\user.php on line 119 B?ez-L?pez, David ; B?ez Villegas, David Alfredo Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning 9781032117676
Introduction to Python: With Applications in Optimization, Image and Video Processing, and Machine Learning, B?ez-L?pez, David ; B?ez Villegas, David Alfredo
Автор: Brunton, Steven L. (university Of Washington) Kutz Название: Data-driven science and engineering ISBN: 1009098489 ISBN-13(EAN): 9781009098489 Издательство: Cambridge Academ Рейтинг: Цена: 7918.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Data-driven discovery is revolutionizing how we model, predict, and control complex systems. This text integrates emerging machine learning and data science methods for engineering and science communities. Now with Python and MATLAB (R), new chapters on reinforcement learning and physics-informed machine learning, and supplementary videos and code.
Автор: Hazan, Elad Название: Introduction to online convex optimization, second edition ISBN: 0262046989 ISBN-13(EAN): 9780262046985 Издательство: Random House (USA) Рейтинг: Цена: 5518.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The Diesel That Did It tells the story of the legendary diesel-electric locomotive, the FT.
Описание: This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for users without any prior programming knowledge.
Описание: This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for users without any prior programming knowledge.
Автор: Boyd Stephen Название: Introduction to Applied Linear Algebra ISBN: 1316518965 ISBN-13(EAN): 9781316518960 Издательство: Cambridge Academ Рейтинг: Цена: 6811.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: A groundbreaking introductory textbook covering the linear algebra methods needed for data science and engineering applications. It combines straightforward explanations with numerous practical examples and exercises from data science, machine learning and artificial intelligence, signal and image processing, navigation, control, and finance.
Inside this book you will find all the basic notions to start with Python and all the programming concepts to build machine learning models. With our proven strategies you will write efficient Python codes in less than a week!
Описание: Have you always wanted to learn computer programming but you're worried it will take too long? Would you like to automate something simple with your PC but you don't know how to do it? Or maybe you know other programming languages and are interested in learning Python quickly?
As a beginner you might think that programming is difficult and the possibility to give up before mastering it could be high... So, if you have a project to develop you could think on hiring a programmer to shorten the time. This may seem like a good idea but it is certainly very expensive. Otherwise you could waste your time pursuing tutorials online.
The best solution is to follow a complete programming manual with hands-on projects and practical exercises.
What you will find inside and a quick overview of the main topics:
✓ Why Python is considered the best programming language for a beginner
✓ The most common mistakes to avoid when you start programming
✓ BOOK 1: PYTHON PROGRAMMING
- The 7 built-in functions to make your life easier while coding a software program
- The program you need to develop your first own application
✓ BOOK 2: PYTHON MACHINE LEARNING
- The algorithms that will make your life easier
- The 2 libraries you need implementing to develop the desired ML models
✓ BOOK 3: PYTHON DATA SCIENCE
- 3 actions required to gain insights from big data
- A simple method to implement predictive analytics
✓ Some projects to write Python codes in less than a week
✓ Quizzes at the end of every chapter to review immediately what you've learned
Why is this book different?
Computer Programming Academy structured these guides as a course with seven chapters for seven days with special exercises for each section.This protocol, tested on both beginners and people who were already familiar with coding, takes advantage of the principle of diving, concentrating learning in one week. The result? The content of the course was learned faster and remembered longer.
Even if you're completely new to programming in 2020 or you are just looking to widen your skills as programmer this book is perfect for you.
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Автор: Korstanje, Joos Название: Machine learning on geographical data using python ISBN: 1484282868 ISBN-13(EAN): 9781484282861 Издательство: Springer Рейтинг: Цена: 6707.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Get up and running with the basics of geographic information systems (GIS), geospatial analysis, and machine learning on spatial data in Python. This book starts with an introduction to geodata and covers topics such as GIS and common tools, standard formats of geographical data, and an overview of Python tools for geodata. Specifics and difficulties one may encounter when using geographical data are discussed: from coordinate systems and map projections to different geodata formats and types such as points, lines, polygons, and rasters. Analytics operations typically applied to geodata are explained such as clipping, intersecting, buffering, merging, dissolving, and erasing, with implementations in Python. Use cases and examples are included. The book also focuses on applying more advanced machine learning approaches to geographical data and presents interpolation, classification, regression, and clustering via examples and use cases. This book is your go-to resource for machine learning on geodata. It presents the basics of working with spatial data and advanced applications. Examples are presented using code (accessible at github.com/Apress/machine-learning-geographic-data-python) and facilitate learning by application. What You Will Learn * Understand the fundamental concepts of working with geodata * Work with multiple geographical data types and file formats in Python * Create maps in Python * Apply machine learning on geographical data Who This Book Is For Readers with a basic understanding of machine learning who wish to extend their skill set to analysis of and machine learning on spatial data while remaining in a common data science Python environment
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