Автор: Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong Название: Mathematics for Machine Learning ISBN: 110845514X ISBN-13(EAN): 9781108455145 Издательство: Cambridge Academ Рейтинг: Цена: 42230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.
Автор: Goodfellow Ian, Bengio Yoshua, Courville Aaron Название: Deep Learning ISBN: 0262035618 ISBN-13(EAN): 9780262035613 Издательство: MIT Press Рейтинг: Цена: 90290.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание:
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives.
"Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." -- Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX
Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.
The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.
Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Machine Learning Guide for Oil and Gas Using Python: A Step-by-Step Breakdown with Data, Algorithms, Codes, and Applications delivers a critical training and resource tool to help engineers understand machine learning theory and practice, specifically referencing use cases in oil and gas. The reference moves from explaining how Python works to step-by-step examples of utilization in various oil and gas scenarios, such as well testing, shale reservoirs and production optimization. Petroleum engineers are quickly applying machine learning techniques to their data challenges, but there is a lack of references beyond the math or heavy theory of machine learning. Machine Learning Guide for Oil and Gas Using Python details the open-source tool Python by explaining how it works at an introductory level then bridging into how to apply the algorithms into different oil and gas scenarios. While similar resources are often too mathematical, this book balances theory with applications, including use cases that help solve different oil and gas data challenges.
Автор: Raschka, Sebastian Mirjalili, Vahid Название: Python machine learning - ISBN: 1787125939 ISBN-13(EAN): 9781787125933 Издательство: Неизвестно Цена: 53940.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This second edition of Python Machine Learning by Sebastian Raschka is for developers and data scientists looking for a practical approach to machine learning and deep learning. In this updated edition, you`ll explore the machine learning process using Python and the latest open source technologies, including scikit-learn and TensorFlow 1.x.
Python is a general-purpose high level programming language that is being increasingly used in data science and in designing machine learning algorithms. This book provides a quick introduction to Python and its libraries like numpy, scipy, pandas, matplotlib and explains how it can be applied to develop machine learning algorithms that solve real world problems. This book starts with an introduction to machine learning with Python language and shows you how to setup Python and its packages. It further covers all important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.
This tutorial also provides various projects that teaches you the techniques and functionalities such as news topic classification, spam email detection, online ad click-through prediction, stock prices forecast and other several important machine learning algorithms.
This tutorial has been prepared for professionals aspiring to learn machine learning with python and develop applications involving machine learning techniques such as recommendation, classification, and clustering. Through this tutorial, you will learn to solve data-driven problems and implement your solutions using the powerful yet simple programming language, Python and its packages. After completing this tutorial, you will gain a broad picture of the machine learning environment and the best practices and techniques for machine learning algorithms in python .
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!
Автор: Academy Computer Programming Название: Python: This Book Includes: Programming, Machine Learning and Data Science. An Hands-On Introduction to Python Programming Lan ISBN: 1914185161 ISBN-13(EAN): 9781914185168 Издательство: Неизвестно Рейтинг: Цена: 26620.00 T Наличие на складе: Есть у поставщика Поставка под заказ. 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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Do you Want to learn more about Python Programming, Machine Learning and Artificial Intelligence ?.... then read on.
Python is a powerful programming language that can be used for the development of various types of applications. It is an Object-Oriented Programming language and it is interpreted rather than being compiled.
Python is considered to be among the most beloved programming languages in any circle of programmers. Software engineers, hackers, and Data Scientists alike are in love with the versatility that Python has to offer. Besides, the Object-Oriented feature of Python coupled with its flexibility is also some of the major attractions for this language. Programmers are now developing a wide range of mobile as well as web applications that we enjoy on an everyday basis.
Python Programming Crash Course doesn't make any assumptions about your background or knowledge of Python or computer programming. You need no prior knowledge to benefit from this book. You will be guided step by step using a logical and systematic approach. As new concepts, commands, or jargon are encountered they are explained in plain language, making it easy for anyone to understand.
In this Book you will learning:
Introduction ito iPython
Variables
Operators
Loops
Functions
Object-Oriented iProgramming-OOP
Modules
File ihandling
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Автор: Gupta Pramod, Sehgal Naresh K. Название: Introduction to Machine Learning in the Cloud with Python: Concepts and Practices ISBN: 3030712699 ISBN-13(EAN): 9783030712693 Издательство: Springer Цена: 55890.00 T Наличие на складе: Поставка под заказ. Описание: This book provides an introduction to machine learning and cloud computing, both from a conceptual level, along with their usage with underlying infrastructure.
Автор: Korstanje, Joos Название: Machine learning on geographical data using python ISBN: 1484282868 ISBN-13(EAN): 9781484282861 Издательство: Springer Рейтинг: Цена: 51230.00 T 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
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data.
The updated edition of this practical book uses concrete examples, minimal theory, and three production-ready Python frameworks--scikit-learn, Keras, and TensorFlow--to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. You'll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started.
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