Luca Massaron - Böcker

Dive into the intelligence that powers artificial intelligence Artificial intelligence is swiftly moving from a sci-fi future to a modern reality. This edition of Artificial Intelligence For Dummies keeps pace with the lighting-fast expansion of AI tools that are overhauling every corner of reality. This book demystifies how artificial intelligence systems operate, giving you a look at the inner workings of AI and explaining the important role of data in creating intelligence. You'll get a primer on using AI in everyday life, and you'll also get a glimpse into possible AI-driven futures. What's next for humanity in the age of AI? How will your job and your life change as AI continue to evolve? How can you take advantage of AI today to make your live easier? This jargon-free Dummies guide answers all your most pressing questions about the world of artificial intelligence. Learn the basics of AI hardware and software, and how intelligence is created from codeGet up to date with the latest AI trends and disruptions across industriesWrap your mind around what the AI revolution means for humanity, and for youDiscover tips on using generative AI ethically and effectivelyArtificial Intelligence For Dummies is the ideal starting point for anyone seeking a deeper technological understanding of how artificial intelligence works and what promise it holds for the future.

Business runs on tabular data in databases, spreadsheets, and logs. Crunch that data using deep learning, gradient boosting, and other machine learning techniques.Every organization in the world stores data in tables. Advanced Analytics for Business reveals practical techniques for applying machine learning techniques like deep learning and gradient boosting to your company's rows and columns.Inside Advanced Analytics for Business you'll learn how to:

 

Pick the right machine learning approach for your data Apply deep learning to tabular data Deploy tabular machine learning locally and in the cloud Pipelines to automatically train and maintain a model

This book collects best practices, hard-won tips and tricks, and hands-on techniques for making sense of tabular data using advanced machine learning techniques. Inside, you'll discover how to use XGBoost and LightGBM on tabular data, optimize deep learning libraries like TensorFlow and PyTorch for tabular data, and use cloud tools like Vertex AI to create an automated MLOps pipeline.

A comprehensive roadmap to using AI in your career and in your life Artificial intelligence is everywhere. Major software organizations like Microsoft, Google, and Apple have built AI directly into products and invited the world to become part of the AI revolution. And it's impossible to use these tools to their fullest potential without understanding the basics of what AI is and what it can do. Artificial Intelligence All-in-One For Dummies compiles insight from the expert authors of AI books in the For Dummies series to provide an easy-to-follow walkthrough for anyone interested in learning how to use AI. You'll learn how to put artificial intelligence to work for you and your company in a wide variety of situations, from creating office assistants to managing projects and marketing your products. Inside the book: How to prompt AI platforms like ChatGPT and Copilot while avoiding “hallucinations” and other bugsStrategies for adding artificial intelligence tools to your company's existing workflows to improve efficiency and generate new opportunitiesTechniques to improve your programming capabilities with AI or create new AI-powered toolsPerfect for professionals curious about the potential and pitfalls associated with generative artificial intelligence, Artificial Intelligence All-in-One For Dummies shows you exactly how AI works and how you can apply it in your own professional and personal life.

Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.    In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample codeProvides real-world examples within the approachable textOffers hands-on activities to make learning easierShows you how to use Deep Learning more effectively with the right toolsThis book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Your logical, linear guide to the fundamentals of data science programmingData science is exploding—in a good way—with a forecast of 1.7 megabytes of new information created every second for each human being on the planet by 2020 and 11.5 million job openings by 2026. It clearly pays dividends to be in the know. This friendly guide charts a path through the fundamentals of data science and then delves into the actual work: linear regression, logical regression, machine learning, neural networks, recommender engines, and cross-validation of models.Data Science Programming All-In-One For Dummies is a compilation of the key data science, machine learning, and deep learning programming languages: Python and R. It helps you decide which programming languages are best for specific data science needs. It also gives you the guidelines to build your own projects to solve problems in real time. Get grounded: the ideal start for new data professionalsWhat lies ahead: learn about specific areas that data is transforming  Be meaningful: find out how to tell your data storySee clearly: pick up the art of visualizationWhether you’re a beginning student or already mid-career, get your copy now and add even more meaning to your life—and everyone else’s!

One of Mark Cuban’s top reads for better understanding A.I. (inc.com, 2021) Your comprehensive entry-level guide to machine learningWhile machine learning expertise doesn’t quite mean you can create your own Turing Test-proof android—as in the movie Ex Machina—it is a form of artificial intelligence and one of the most exciting technological means of identifying opportunities and solving problems fast and on a large scale. Anyone who masters the principles of machine learning is mastering a big part of our tech future and opening up incredible new directions in careers that include fraud detection, optimizing search results, serving real-time ads, credit-scoring, building accurate and sophisticated pricing models—and way, way more.Unlike most machine learning books, the fully updated 2nd Edition of Machine Learning For Dummies doesn't assume you have years of experience using programming languages such as Python (R source is also included in a downloadable form with comments and explanations), but lets you in on the ground floor, covering the entry-level materials that will get you up and running building models you need to perform practical tasks. It takes a look at the underlying—and fascinating—math principles that power machine learning but also shows that you don't need to be a math whiz to build fun new tools and apply them to your work and study. Understand the history of AI and machine learningWork with Python 3.8 and TensorFlow 2.x (and R as a download)Build and test your own modelsUse the latest datasets, rather than the worn out data found in other booksApply machine learning to real problemsWhether you want to learn for college or to enhance your business or career performance, this friendly beginner's guide is your best introduction to machine learning, allowing you to become quickly confident using this amazing and fast-developing technology that's impacting lives for the better all over the world.

Your secret weapon to understanding—and using!—one of the most powerful influences in the world todayFrom your Facebook News Feed to your most recent insurance premiums—even making toast!—algorithms play a role in virtually everything that happens in modern society and in your personal life. And while they can seem complicated from a distance, the reality is that, with a little help, anyone can understand—and even use—these powerful problem-solving tools!In Algorithms For Dummies, you'll discover the basics of algorithms, including what they are, how they work, where you can find them (spoiler alert: everywhere!), who invented the most important ones in use today (a Greek philosopher is involved), and how to create them yourself.You'll also find: Dozens of graphs and charts that help you understand the inner workings of algorithmsLinks to an online repository called GitHub for constant access to updated codeStep-by-step instructions on how to use Google Colaboratory, a zero-setup coding environment that runs right from your browserWhether you're a curious internet user wondering how Google seems to always know the right answer to your question or a beginning computer science student looking for a head start on your next class, Algorithms For Dummies is the can't-miss resource you've been waiting for.

Let Python do the heavy lifting for you as you analyze large datasets Python for Data Science For Dummies lets you get your hands dirty with data using one of the top programming languages. This beginner’s guide takes you step by step through getting started, performing data analysis, understanding datasets and example code, working with Google Colab, sampling data, and beyond. Coding your data analysis tasks will make your life easier, make you more in-demand as an employee, and open the door to valuable knowledge and insights. This new edition is updated for the latest version of Python and includes current, relevant data examples. Get a firm background in the basics of Python coding for data analysisLearn about data science careers you can pursue with Python coding skillsIntegrate data analysis with multimedia and graphics Manage and organize data with cloud-based relational databasesPython careers are on the rise. Grab this user-friendly Dummies guide and gain the programming skills you need to become a data pro.

Install data analytics into your brain with this comprehensive introductionData Analytics & Visualization All-in-One For Dummies collects the essential information on mining, organizing, and communicating data, all in one place. Clocking in at around 850 pages, this tome of a reference delivers eight books in one, so you can build a solid foundation of knowledge in data wrangling. Data analytics professionals are highly sought after these days, and this book will put you on the path to becoming one. You’ll learn all about sources of data like data lakes, and you’ll discover how to extract data using tools like Microsoft Power BI, organize the data in Microsoft Excel, and visually present the data in a way that makes sense using a Tableau. You’ll even get an intro to the Python, R, and SQL coding needed to take your data skills to a new level. With this Dummies guide, you’ll be well on your way to becoming a priceless data jockey. Mine data from data sourcesOrganize and analyze data Use data to tell a story with TableauExpand your know-how with Python and RNew and novice data analysts will love this All-in-One reference on how to make sense of data. Get ready to watch as your career in data takes off.

The most human-friendly book on machine learning Somewhere buried in all the systems that drive artificial intelligence, you'll find machine learning—the process that allows technology to build knowledge based on data and patterns. Machine Learning For Dummies is an excellent starting point for anyone who wants deeper insight into how all this learning actually happens. This book offers an overview of machine learning and its most important practical applications. Then, you'll dive into the tools, code, and math that make machine learning go—and you'll even get step-by-step instructions for testing it out on your own. For an easy-to-follow introduction to building smart algorithms, this Dummies guide is your go-to. Piece together what machine learning is, what it can do, and what it can't doLearn the basics of machine learning code and how it integrates with large datasetsUnderstand the mathematical principles that AI uses to make itself smarterConsider real-world applications of machine learning and write your own algorithmsWith clear explanations and hands-on instruction, Machine Learning For Dummies is a great entry-level resource for developers looking to get started with AI and machine learning.

Learn to build powerful machine learning models quickly and deploy large-scale predictive applicationsAbout This Book• Design, engineer and deploy scalable machine learning solutions with the power of Python• Take command of Hadoop and Spark with Python for effective machine learning on a map reduce framework • Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is ForThis book is for anyone who intends to work with large and complex data sets. Familiarity with basic Python and machine learning concepts is recommended. Working knowledge in statistics and computational mathematics would also be helpful.What You Will Learn• Apply the most scalable machine learning algorithms • Work with modern state-of-the-art large-scale machine learning techniques• Increase predictive accuracy with deep learning and scalable data-handling techniques• Improve your work by combining the MapReduce framework with Spark• Build powerful ensembles at scale• Use data streams to train linear and non-linear predictive models from extremely large datasets using a single machineIn DetailLarge Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.Style and approachThis efficient and practical title is stuffed full of the techniques, tips and tools you need to ensure your large scale Python machine learning runs swiftly and seamlessly.Large-scale machine learning tackles a different issue to what is currently on the market. Those working with Hadoop clusters and in data intensive environments can now learn effective ways of building powerful machine learning models from prototype to production. This book is written in a style that programmers from other languages (R, Julia, Java, Matlab) can follow.

Learn to solve challenging data science problems by building powerful machine learning models using PythonAbout This Book* Understand which algorithms to use in a given context with the help of this exciting recipe-based guide* This practical tutorial tackles real-world computing problems through a rigorous and effective approach* Build state-of-the-art models and develop personalized recommendations to perform machine learning at scaleWho This Book Is ForThis Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected.What You Will Learn* Use predictive modeling and apply it to real-world problems* Understand how to perform market segmentation using unsupervised learning* Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test* Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms* Increase predictive accuracy with deep learning and scalable data-handling techniques* Work with modern state-of-the-art large-scale machine learning techniques* Learn to use Python code to implement a range of machine learning algorithms and techniquesIn DetailMachine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us.In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms.The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and feature engineering.The third module in this learning path, Large Scale Machine Learning with Python, dives into scalable machine learning and the three forms of scalability. It covers the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.This Learning Path will teach you Python machine learning for the real world. The machine learning techniques covered in this Learning Path are at the forefront of commercial practice.This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:* Python Machine Learning Cookbook by Prateek Joshi* Advanced Machine Learning with Python by John Hearty* Large Scale Machine Learning with Python by Bastiaan Sjardin, Alberto Boschetti, Luca MassaronStyle and approachThis course is a smooth learning path that will teach you how to get started with Python machine learning for the real world, and develop solutions to real-world problems. Through this comprehensive course, you'll learn to create the most effective machine learning techniques from scratch and more!

Gain useful insights from your data using popular data science toolsKey FeaturesA one-stop guide to Python libraries such as pandas and NumPyComprehensive coverage of data science operations such as data cleaning and data manipulationChoose scalable learning algorithms for your data science tasksBook DescriptionFully expanded and upgraded, the latest edition of Python Data Science Essentials will help you succeed in data science operations using the most common Python libraries. This book offers up-to-date insight into the core of Python, including the latest versions of the Jupyter Notebook, NumPy, pandas, and scikit-learn.The book covers detailed examples and large hybrid datasets to help you grasp essential statistical techniques for data collection, data munging and analysis, visualization, and reporting activities. You will also gain an understanding of advanced data science topics such as machine learning algorithms, distributed computing, tuning predictive models, and natural language processing. Furthermore, You’ll also be introduced to deep learning and gradient boosting solutions such as XGBoost, LightGBM, and CatBoost.By the end of the book, you will have gained a complete overview of the principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business usersWhat you will learnSet up your data science toolbox on Windows, Mac, and LinuxUse the core machine learning methods offered by the scikit-learn libraryManipulate, fix, and explore data to solve data science problemsLearn advanced explorative and manipulative techniques to solve data operationsOptimize your machine learning models for optimized performanceExplore and cluster graphs, taking advantage of interconnections and links in your dataWho this book is forIf you’re a data science entrant, data analyst, or data engineer, this book will help you get ready to tackle real-world data science problems without wasting any time. Basic knowledge of probability/statistics and Python coding experience will assist you in understanding the concepts covered in this book.

Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and moreKey FeaturesDeep Learning solutions from Kaggle Masters and Google Developer ExpertsGet to grips with the fundamentals including variables, matrices, and data sourcesLearn advanced techniques to make your algorithms faster and more accurateBook DescriptionThe independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. Dive into recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google’s machine learning library, TensorFlow.This cookbook covers the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You’ll discover real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and regression.Explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be used to solve computer vision and natural language processing (NLP) problems.With the help of this book, you will be proficient in using TensorFlow, understand deep learning from the basics, and be able to implement machine learning algorithms in real-world scenarios.What you will learnTake TensorFlow into productionImplement and fine-tune Transformer models for various NLP tasksApply reinforcement learning algorithms using the TF-Agents frameworkUnderstand linear regression techniques and use Estimators to train linear modelsExecute neural networks and improve predictions on tabular dataMaster convolutional neural networks and recurrent neural networks through practical recipesWho this book is forIf you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you.Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

Get a step ahead of your competitors with insights from over 30 Kaggle Masters and Grandmasters. Discover tips, tricks, and best practices for competing effectively on Kaggle and becoming a better data scientist.Purchase of the print or Kindle book includes a free eBook in the PDF format.Key FeaturesLearn how Kaggle works and how to make the most of competitions from over 30 expert KagglersSharpen your modeling skills with ensembling, feature engineering, adversarial validation and AutoMLA concise collection of smart data handling techniques for modeling and parameter tuningBook DescriptionMillions of data enthusiasts from around the world compete on Kaggle, the most famous data science competition platform of them all. Participating in Kaggle competitions is a surefire way to improve your data analysis skills, network with an amazing community of data scientists, and gain valuable experience to help grow your career.The first book of its kind, The Kaggle Book assembles in one place the techniques and skills you’ll need for success in competitions, data science projects, and beyond. Two Kaggle Grandmasters walk you through modeling strategies you won’t easily find elsewhere, and the knowledge they’ve accumulated along the way. As well as Kaggle-specific tips, you’ll learn more general techniques for approaching tasks based on image, tabular, textual data, and reinforcement learning. You’ll design better validation schemes and work more comfortably with different evaluation metrics.Whether you want to climb the ranks of Kaggle, build some more data science skills, or improve the accuracy of your existing models, this book is for you.Plus, join our Discord Community to learn along with more than 1,000 members and meet like-minded people!What you will learnGet acquainted with Kaggle as a competition platformMake the most of Kaggle Notebooks, Datasets, and Discussion forumsCreate a portfolio of projects and ideas to get further in your careerDesign k-fold and probabilistic validation schemesGet to grips with common and never-before-seen evaluation metricsUnderstand binary and multi-class classification and object detectionApproach NLP and time series tasks more effectivelyHandle simulation and optimization competitions on KaggleWho this book is forThis book is suitable for anyone new to Kaggle, veteran users, and anyone in between. Data analysts/scientists who are trying to do better in Kaggle competitions and secure jobs with tech giants will find this book useful.A basic understanding of machine learning concepts will help you make the most of this book.

Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises.Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesChallenge yourself to start thinking like a Kaggle GrandmasterFill your portfolio with impressive case studies that will come in handy during interviewsPacked with exercises and notes pages for you to enhance your skills and record key findingsBook DescriptionMore than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they’ve come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist.In this book, you’ll get up close and personal with four extensive case studies based on past Kaggle competitions. You’ll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering.You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.What you will learnTake your modeling to the next level by analyzing different case studiesBoost your data science skillset with a curated selection of exercisesCombine different methods to create better solutionsGet a deeper insight into NLP and how it can help you solve unlikely challengesSharpen your knowledge of time-series forecastingChallenge yourself to become a better data scientistWho this book is forIf you’re new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite.This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.

Stay one step ahead of your competitors with proven tips, strategies, and insights from over 30 Kaggle Masters and Grandmasters and become a better data scientist.This new edition features updated content and new chapters on Kaggle Models, time series, and Generative AI competitions. Key FeaturesLearn how Kaggle works to make the most of every competition with winning strategies from 30+ expert KagglersSharpen your modeling skills with feature engineering, adversarial validation, gradient boosting, tabular deep learning, ensembling, and AutoMLMaster data handling techniques for smarter modeling and parameter tuningPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionKaggle has become the proving ground for millions of data enthusiasts worldwide, offering what no classroom tutorial can match: battle-tested skills built through real-world challenges and the hands-on experience that employers seek. Every competition sharpens your data analysis skills, expands your network within the data scientist community, and gives compelling proof of expertise to unlock career opportunities.The first book of its kind, The Kaggle Book brings together everything you need to excel in competitions, data science projects, and beyond. This new edition includes fresh content and new chapters on Kaggle Models, time series, and Generative AI competitions, with three Kaggle Grandmasters guiding you through modeling strategies and sharing hard-earned insights accumulated over years of competition.The book extends far past competition tactics, revealing techniques for tackling image, tabular, and textual data as well as reinforcement learning tasks. You’ll also discover tips for designing better validation schemes and working confidently with both standard and unconventional evaluation metrics.Whether you want to climb the Kaggle leaderboard, accelerate your data science career, or improve the accuracy of your models, this book is for you.Join our Discord community of over 1,000 members to learn, share, and grow together!What you will learnGet acquainted with Kaggle as a competition platformMake the most of Kaggle Notebooks, Datasets, Models and Discussion forumsBuild a compelling portfolio of projects and ideas to advance your careerUnderstand binary and multi-class classification, as well as object detectionApproach NLP and time series problems with greater efficiencyDesign k-fold and probabilistic validation schemes and experiment with multiple approachesGet to grips with common and never-before-seen evaluation metricsHandle simulation, optimization, and the new Generative AI competitions on KaggleWho this book is forThis book is for anyone interested in Kaggle, whether you’re just starting out, a veteran user, or somewhere in between. Data analysts and data scientists looking to improve their performance in Kaggle competitions and improve their job prospects with tech giants will find this book useful.A basic understanding of machine learning concepts will help you get the most out of this book.

Maschinelles Lernen ist aufregend: Mit schnellen Prozessoren und großen Speichern können Computer aus Erfahrungen lernen, künstliche Intelligenz kommt wieder in Reichweite. Mit diesem Buch verstehen Sie, was maschinelles Lernen bedeutet, für welche Probleme es sich eignet, welche neuen Herangehensweisen damit möglich sind und wie Sie mit Python, R und speziellen Werkzeugen maschinelles Lernen implementieren. Sie brauchen dafür keine jahrelange Erfahrung als Programmierer und kein Mathematikstudium. Die praktische Anwendung maschinellen Lernens steht in diesem Buch im Vordergrund. Spielen Sie mit den Tools und haben Sie Spaß dabei! Lernen Sie Fakten und Mythen zum maschinellen Lernen zu unterscheiden.

Wir leben in einer algorithmenbestimmten Welt. Deshalb lohnt es sich zu verstehen, wie Algorithmen arbeiten. Das Buch präsentiert die wichtigsten Anwendungsgebiete für Algorithmen: Optimierung, Sortiervorgänge, Graphentheorie, Textanalyse, Hashfunktionen. Zu jedem Algorithmus werden jeweils Hintergrundwissen und praktische Grundlagen vermittelt sowie Beispiele für aktuelle Anwendungen gegeben. Für interessierte Leser gibt es Umsetzungen in Python, sodass die Algorithmen auch verändert und die Auswirkungen der Veränderungen beobachtet werden können. Dieses Buch richtet sich an Menschen, die an Algorithmen interessiert sind, ohne eine Doktorarbeit zu dem Thema schreiben zu wollen. Wer es gelesen hat, versteht, wie wichtige Algorithmen arbeiten und wie man von dieser Arbeit beispielsweise bei der Entwicklung von Unternehmensstrategien profitieren kann.

Wollen Sie sich über Deep Learning informieren und vielleicht erste kleine Schritte machen, diese Technologie zu nutzen? Dann hilft Ihnen dieses Buch. Die Autoren erklären Ihnen, welchen Einfluss Deep Learning im Alltag hat und in welcher Beziehung es zu maschinellem Lernen steht. Sie sammeln erste eigene Erfahrungen mit vorgegebenen Python-Programmen und verstehen so die Funktionsweise von neuronalen Netzen und wie Bilder klassifiziert und Sprache sowie Text verarbeitet werden. So ist dieses Buch ein schneller erster und verständlicher Einstieg in Deep Learning.