If you’re one of those new people, then we heartily welcome you to the latest Raspberry Pi Official Projects Book. With 200 pages of excellent guides, inspiring projects, and informative reviews, it should keep you busy learning about all the amazing things you can do with your Raspberry Pi. We even have a Getting Started guide if you’re trying to figure out where to begin. For Pi veterans, there are some truly challenging builds to get stuck into as well.

lgli/The MagPi;The Official Raspberry Pi Projects Book ;;Volume 2;The MagPi;2016;;;English.pdf

lgrsnf/The MagPi;The Official Raspberry Pi Projects Book ;;Volume 2;The MagPi;2016;;;English.pdf

zlib/Computers/Hardware/The MagPi/The Official Raspberry Pi Projects Book_2828263.pdf

Python Machine Learning : Learn How to Build Powerful Python Machine Learning Algorithms to Generate Useful Data Insights with This Data Analysis Tutorial

Python machine learning : unlock deeper insights into machine learning with this vital guide to cutting-edge predictive analytics

Карьера программиста: прохождение собеседования, разработка программного обеспечения, структуры данных и алгоритмы: 16+

Python и машинное обучение: наука и искусство построения алгоритмов, которые извлекают знания из данных

Cracking the coding interview : 189 programming interview questions and solutions

Python Machine Learning, 1st Edition

200 Pages of Coding and Creating

Г. Лакман Макдауэлл; перевел с английского Е. Матвеев

Себастьян Рашка; перевод с англ. А. В. Логунова

The MagPi Team; Russell Barnes

McDowell, Gayle Laakmann

Gayle Laakmann McDowell

Raspberry Pi Foundation

Макдауэлл, Гэйл Лакман

Raschka, Sebastian

Sebastian Raschka

Рашка, Себастьян

Raspberry Pi (Trading) Ltd

Select Publisher Services

Packt Publishing Limited

CareerCup, LLC

ДМК Пресс

Google

Питер

Community experience distilled, Community experience distilled, England, 2016

Библиотека программиста, 6-е изд., Санкт-Петербург [и др.], Russia, 2022

Библиотека программиста, 6-е изд., Санкт-Петербург [и др.], Russia, 2020

Official raspberry pi projects book, Cambridge, UK, 2017

Community experience distilled, Birmingham, UK, 2015

Sixth edition, Palo Alto, California, 2016

United Kingdom and Ireland, United Kingdom

United States, United States of America

Packt Publishing, Birmingham, UK, 2015

Цветное издание, Москва, Russia, 2017

6th edition, Palo Alto, CA, 2016

6th edition, Palo Alto, CA, 2015

1st edition, 2015

07/01/2015

lg1584888

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Includes index.

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Пер.: McDowell, Gayle Laakmann (1982-) Cracking the coding interview 6th ed. Palo Alto, Calif : CareerCup, cop. 2015 978-0984782857

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<p>Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics<br></p><p>About This Book<br></p><ul> <li>Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization </li> <li>Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms </li> <li>Ask – and answer – tough questions of your data with robust statistical models, built for a range of datasets </li></ul><p>Who This Book Is For<br></p><p>If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.<br></p><p>What You Will Learn<br></p><ul> <li>Explore how to use different machine learning models to ask different questions of your data </li> <li>Learn how to build neural networks using Keras and Theano </li> <li>Find out how to write clean and elegant Python code that will optimize the strength of your algorithms </li> <li>Discover how to embed your machine learning model in a web application for increased accessibility </li> <li>Predict continuous target outcomes using regression analysis </li> <li>Uncover hidden patterns and structures in data with clustering </li> <li>Organize data using effective pre-processing techniques </li> <li>Get to grips with sentiment analysis to delve deeper into textual and social media data </li></ul><p>In Detail<br></p><p>Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.<br></p><p>Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization.<br></p><p>Style and approach<br></p><p>Python Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models.<br></p>

Now In The 6th Edition, The Book Gives You The Interview Preparation You Need To Get The Top Software Developer Jobs. This Is A Deeply Technical Book And Focuses On The Software Engineering Skills To Ace Your Interview. The Book Includes 189 Programming Interview Questions And Answers, As Well As Other Advice. 1. The Interview Process. Why? -- How Questions Are Selected -- Timeline And Preparation Map -- It's All Relative -- Frequently Asked Questions -- 2. Behind The Scenes. The Microsoft Interview -- The Amazon Interview -- The Google Interview -- The Apple Interview -- The Facebook Interview -- The Palantir Interview -- 3. Special Situations. Experienced Candidates -- Testers And Sdets -- Product (and Program) Managment --dev Lead And Managers -- Start-ups -- Acquisitions And Acquihires -- For Interviewers -- 4. Before The Interview. Getting The Right Experience -- Writing A Great Resume -- Preparation Map -- 5. Behavioral Questions. Interview Preparation Grid -- Know Your Technical Projects -- Responding To Behavioral Questions -- So, Tell Me About Yourself -- 6. Big O. An Analogy -- Time Complexity -- Space Complexity -- Drop The Constraints -- Drop The Non-dominant Terms -- Multi-part Algorithms: Add Vs. Multiply -- Amortized Time -- Log N Runtimes -- Recursive Runtimes --^ Examples And Exercises -- 7. Technical Questions. How To Prepare -- What You Need To Know -- Walking Through A Problem -- Optimize & Solve Technique # 1: Look For Bud -- Optimize & Solve Technique #2: Diy (do It Yourself) -- Optimize & Solve Technique #3: Simplify And Generalize -- Optimize & Solve Technique #4: Base Case And Build -- Optimize & Solve Technique #5: Data Structure Brainstorm -- Best Conceivable Runtime (bcr) -- Handling Incorrect Answers -- When You've Heard A Question Before -- The 'perfect' Language For Interviews -- What Good Coding Looks Like -- Don't Give Up! -- 8. The Offer And Beyond. Handling Offers And Rejection -- Evaluating The Offer -- Negotiation -- On The Job -- 9. Interview Questions. Data Structures: Arrays And Strings ; Linked Lists ; Stacks And Queues ; Trees And Graphs --^ Concepts And Algorithms. Bit Manipulation ; Math And Logic Puzzles ; Object-oriented Design ; Recursion And Dynamic Programming ; System Design And Scalability ; Sorting And Searching ; Testing -- Knowledge Based. C And C++ ; Java ; Databases ; Threads And Locks ; Additional Review Problems. Moderate ; Hard -- 10. Solutions. Data Structures -- Concepts And Algorithms -- Knowledge Based -- Additional Review Problems -- 11. Advanced Topics. Useful Math -- Topological Sort -- Dijkstra's Algorithm -- Hash Table Collision Resolution -- Rabin-karp Substring Search -- Avl Trees -- Red-black Trees -- Mapreduce -- Additional Studying -- 12. Code Library. Hashmaplist <t, E> -- Treenode (binary Search Tree) -- Linkedlistnode (linked List) -- Trie & Trienode -- 13. Hints. Hints For Data Structures -- Hints For Concepts And Algorithms -- Hints For Knowledge-based Questions -- Hints For Assorted Review Problems -- 14. About The Author. Gayle Laakmann Mcdowell, Founder And Ceo, Careercup.com.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsAbout This BookLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk - and answer - tough questions of your data with robust statistical models, built for a range of datasetsWho This Book Is ForIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning - whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will LearnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Pylearn 2 and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataIn DetailMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data - its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. Style and approachPython Machine Learning connects the fundamental theoretical principles behind machine learning to their practical application in a way that focuses you on asking and answering the right questions. It walks you through the key elements of Python and its powerful machine learning libraries, while demonstrating how to get to grips with a range of statistical models

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analyticsKey FeaturesLeverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualizationLearn effective strategies and best practices to improve and optimize machine learning systems and algorithmsAsk – and answer – tough questions of your data with robust statistical models, built for a range of datasetsBook DescriptionMachine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data – its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer some of the most important questions facing you and your organization. What you will learnExplore how to use different machine learning models to ask different questions of your dataLearn how to build neural networks using Keras and TheanoFind out how to write clean and elegant Python code that will optimize the strength of your algorithmsDiscover how to embed your machine learning model in a web application for increased accessibilityPredict continuous target outcomes using regression analysisUncover hidden patterns and structures in data with clusteringOrganize data using effective pre-processing techniquesGet to grips with sentiment analysis to delve deeper into textual and social media dataWho this book is forIf you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning – whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource.

Unlock deeper insights into Machine Leaning with this vital guide to cutting-edge predictive analytics About This Book Leverage Python's most powerful open-source libraries for deep learning, data wrangling, and data visualization Learn effective strategies and best practices to improve and optimize machine learning systems and algorithms Ask -- and answer -- tough questions of your data with robust statistical models, built for a range of datasets Who This Book Is For If you want to find out how to use Python to start answering critical questions of your data, pick up Python Machine Learning -- whether you want to get started from scratch or want to extend your data science knowledge, this is an essential and unmissable resource. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build neural networks using Pylearn 2 and Theano Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Predict continuous target outcomes using regression analysis Uncover hidden patterns and structures in data with clustering Organize data using effective pre-processing techniques Get to grips with sentiment analysis to delve deeper into textual and social media data In Detail Machine learning and predictive analytics are transforming the way businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data -- its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success. Python Machine Learning gives you access to the world of predictive analytics and demonstrates why Python is one of the world's leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science book is invaluable. Covering a wide range of powerful Python libraries, including scikit-learn, Theano, and Pylearn2, and featuring guidance and tips on everything from sentiment analysis to neural networks, you'll soon be able to answer som..

I am not a recruiter. I am a software engineer. And as such, I know what it's like to be asked to whip up brilliant algorithms on the spot and then write flawless code on a whiteboard. I've been through this as a candidate and as an interviewer.
Cracking the Coding Interview, 6th Edition is here to help you through this process, teaching you what you need to know and enabling you to perform at your very best. I've coached and interviewed hundreds of software engineers. The result is this book.
Learn how to uncover the hints and hidden details in a question, discover how to break down a problem into manageable chunks, develop techniques to unstick yourself when stuck, learn (or re-learn) core computer science concepts, and practice on 189 interview questions and solutions.
These interview questions are real; they are not pulled out of computer science textbooks. They reflect what's truly being asked at the top companies, so that you can be as prepared as possible.
WHAT'S INSIDE?
- 189 programming interview questions, ranging from the basics to the trickiest algorithm problems.
- A walk-through of how to derive each solution, so that you can learn how to get there yourself.
- Hints on how to solve each of the 189 questions, just like what you would get in a real interview.
- Five proven strategies to tackle algorithm questions, so that you can solve questions you haven't seen.
- Extensive coverage of essential topics, such as big O time, data structures, and core algorithms.
- A behind the scenes look at how top companies like Google and Facebook hire developers.
- Techniques to prepare for and ace the soft side of the interview: behavioral questions.
- For interviewers and companies: details on what makes a good interview question and hiring process.

Link to the GitHub Repository containing the code examples and additional material: (https://github.com/rasbt/python-machine-learning-book) https://github.com/rasbt/python-machi...
Many of the most innovative breakthroughs and exciting new technologies can be attributed to applications of machine learning. We are living in an age where data comes in abundance, and thanks to the self-learning algorithms from the field of machine learning, we can turn this data into knowledge. Automated speech recognition on our smart phones, web search engines, e-mail spam filters, the recommendation systems of our favorite movie streaming services machine learning makes it all possible.
Thanks to the many powerful open-source libraries that have been developed in recent years, machine learning is now right at our fingertips. Python provides the perfect environment to build machine learning systems productively.
This book will teach you the fundamentals of machine learning and how to utilize these in real-world applications using Python. Step-by-step, you will expand your skill set with the best practices for transforming raw data into useful information, developing learning algorithms efficiently, and evaluating results.
You will discover the different problem categories that machine learning can solve and explore how to classify objects, predict continuous outcomes with regression analysis, and find hidden structures in data via clustering. You will build your own machine learning system for sentiment analysis and finally, learn how to embed your model into a web app to share with the world

Giving computers the ability to learn from data
Training machine learning algorithms for classification
A tour of machine learning classifiers using Scikit-learn
Building good training sets : data preprocessing
Compressing data via dimensionality reduction
Learning best practices for model evaluation and hyperparameter tuning
Combining different models for ensemble learning
Applying machine learning to sentiment analysis
Embedding a machine learning model into a web application
Predicting continuous target variables with regression analysis
Working with unlabeled data : clustering analysis
Training artificial neural networks for image recognition
Parallelizing neural network training with Theano.

Volume 2 of the Official Raspberry Pi Projects Book offers another 200 pages of ideas, inspiration and guides to help you with your Raspberry Pi! The Raspberry Pi is the best-selling British computer of all time and is known the world over for making incredible hardware and software projects possible. Its also helping to revolutionise computing education. Learn all about the worlds favourite credit card-sized computer in this 200 page book

2016-11-23