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Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
by Akshay R Kulkarni Adarsha Shivananda Anoosh Kulkarni V Adithya KrishnanThis book teaches the practical implementation of various concepts for time series analysis and modeling with Python through problem-solution-style recipes, starting with data reading and preprocessing. It begins with the fundamentals of time series forecasting using statistical modeling methods like AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average), and ARIMA (autoregressive integrated moving-average). Next, you'll learn univariate and multivariate modeling using different open-sourced packages like Fbprohet, stats model, and sklearn. You'll also gain insight into classic machine learning-based regression models like randomForest, Xgboost, and LightGBM for forecasting problems. The book concludes by demonstrating the implementation of deep learning models (LSTMs and ANN) for time series forecasting. Each chapter includes several code examples and illustrations. After finishing this book, you will have a foundational understanding of various concepts relating to time series and its implementation in Python. What You Will LearnImplement various techniques in time series analysis using Python.Utilize statistical modeling methods such as AR (autoregressive), MA (moving-average), ARMA (autoregressive moving-average) and ARIMA (autoregressive integrated moving-average) for time series forecasting Understand univariate and multivariate modeling for time series forecastingForecast using machine learning and deep learning techniques such as GBM and LSTM (long short-term memory) Who This Book Is ForData Scientists, Machine Learning Engineers, and software developers interested in time series analysis.
Time Series Analysis for the State-Space Model with R/Stan
by Junichiro HagiwaraThis book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.
Time Series Analysis on AWS: Learn how to build forecasting models and detect anomalies in your time series data
by Michael HoarauLeverage AWS AI/ML managed services to generate value from your time series dataKey FeaturesSolve modern time series analysis problems such as forecasting and anomaly detectionGain a solid understanding of AWS AI/ML managed services and apply them to your business problemsExplore different algorithms to build applications that leverage time series dataBook DescriptionBeing a business analyst and data scientist, you have to use many algorithms and approaches to prepare, process, and build ML-based applications by leveraging time series data, but you face common problems, such as not knowing which algorithm to choose or how to combine and interpret them. Amazon Web Services (AWS) provides numerous services to help you build applications fueled by artificial intelligence (AI) capabilities. This book helps you get to grips with three AWS AI/ML-managed services to enable you to deliver your desired business outcomes.The book begins with Amazon Forecast, where you'll discover how to use time series forecasting, leveraging sophisticated statistical and machine learning algorithms to deliver business outcomes accurately. You'll then learn to use Amazon Lookout for Equipment to build multivariate time series anomaly detection models geared toward industrial equipment and understand how it provides valuable insights to reinforce teams focused on predictive maintenance and predictive quality use cases. In the last chapters, you'll explore Amazon Lookout for Metrics, and automatically detect and diagnose outliers in your business and operational data.By the end of this AWS book, you'll have understood how to use the three AWS AI services effectively to perform time series analysis.What you will learnUnderstand how time series data differs from other types of dataExplore the key challenges that can be solved using time series dataForecast future values of business metrics using Amazon ForecastDetect anomalies and deliver forewarnings using Lookout for EquipmentDetect anomalies in business metrics using Amazon Lookout for MetricsVisualize your predictions to reduce the time to extract insightsWho this book is forIf you're a data analyst, business analyst, or data scientist looking to analyze time series data effectively for solving business problems, this is the book for you. Basic statistics knowledge is assumed, but no machine learning knowledge is necessary. Prior experience with time series data and how it relates to various business problems will help you get the most out of this book. This guide will also help machine learning practitioners find new ways to leverage their skills to build effective time series-based applications.
Time Series Analysis Using SAS Enterprise Guide (SpringerBriefs in Statistics)
by Timina Liu Shuangzhe Liu Lei ShiThis is the first book to present time series analysis using the SAS Enterprise Guide software. It includes some starting background and theory to various time series analysis techniques, and demonstrates the data analysis process and the final results via step-by-step extensive illustrations of the SAS Enterprise Guide software. This book is a practical guide to time series analyses in SAS Enterprise Guide, and is valuable resource that benefits a wide variety of sectors.
Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation
by Tarek A. AtwanPerform time series analysis and forecasting confidently with this Python code bank and reference manualKey FeaturesExplore forecasting and anomaly detection techniques using statistical, machine learning, and deep learning algorithmsLearn different techniques for evaluating, diagnosing, and optimizing your modelsWork with a variety of complex data with trends, multiple seasonal patterns, and irregularitiesBook DescriptionTime series data is everywhere, available at a high frequency and volume. It is complex and can contain noise, irregularities, and multiple patterns, making it crucial to be well-versed with the techniques covered in this book for data preparation, analysis, and forecasting. This book covers practical techniques for working with time series data, starting with ingesting time series data from various sources and formats, whether in private cloud storage, relational databases, non-relational databases, or specialized time series databases such as InfluxDB. Next, you'll learn strategies for handling missing data, dealing with time zones and custom business days, and detecting anomalies using intuitive statistical methods, followed by more advanced unsupervised ML models. The book will also explore forecasting using classical statistical models such as Holt-Winters, SARIMA, and VAR. The recipes will present practical techniques for handling non-stationary data, using power transforms, ACF and PACF plots, and decomposing time series data with multiple seasonal patterns. Later, you'll work with ML and DL models using TensorFlow and PyTorch. Finally, you'll learn how to evaluate, compare, optimize models, and more using the recipes covered in the book.What you will learnUnderstand what makes time series data different from other dataApply various imputation and interpolation strategies for missing dataImplement different models for univariate and multivariate time seriesUse different deep learning libraries such as TensorFlow, Keras, and PyTorchPlot interactive time series visualizations using hvPlotExplore state-space models and the unobserved components model (UCM)Detect anomalies using statistical and machine learning methodsForecast complex time series with multiple seasonal patternsWho this book is forThis book is for data analysts, business analysts, data scientists, data engineers, or Python developers who want practical Python recipes for time series analysis and forecasting techniques. Fundamental knowledge of Python programming is required. Although having a basic math and statistics background will be beneficial, it is not necessary. Prior experience working with time series data to solve business problems will also help you to better utilize and apply the different recipes in this book.
Time Series Clustering and Classification (Chapman & Hall/CRC Computer Science & Data Analysis)
by Elizabeth Ann Maharaj Pierpaolo D'Urso Jorge CaiadoThe beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts, statisticians, mathematicians, econometricians, computer scientists and many others. At the root of these techniques are algorithms and methods for clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification methods, and machine learning methods. It presents a broad and self-contained overview of techniques for both researchers and students. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including unsupervised and supervised approaches Includes a range of real examples from medicine, finance, environmental science, and more R and MATLAB code, and relevant data sets are available on a supplementary website
Time Series Forecasting in Python
by Marco PeixeiroBuild predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.In Time Series Forecasting in Python you will learn how to: Recognize a time series forecasting problem and build a performant predictive model Create univariate forecasting models that account for seasonal effects and external variables Build multivariate forecasting models to predict many time series at once Leverage large datasets by using deep learning for forecasting time series Automate the forecasting process Time Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You&’ll explore interesting real-world datasets like Google&’s daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow. About the technology You can predict the future—with a little help from Python, deep learning, and time series data! Time series forecasting is a technique for modeling time-centric data to identify upcoming events. New Python libraries and powerful deep learning tools make accurate time series forecasts easier than ever before. About the book Time Series Forecasting in Python teaches you how to get immediate, meaningful predictions from time-based data such as logs, customer analytics, and other event streams. In this accessible book, you&’ll learn statistical and deep learning methods for time series forecasting, fully demonstrated with annotated Python code. Develop your skills with projects like predicting the future volume of drug prescriptions, and you&’ll soon be ready to build your own accurate, insightful forecasts. What's inside Create models for seasonal effects and external variables Multivariate forecasting models to predict multiple time series Deep learning for large datasets Automate the forecasting process About the reader For data scientists familiar with Python and TensorFlow. About the author Marco Peixeiro is a seasoned data science instructor who has worked as a data scientist for one of Canada&’s largest banks. Table of Contents PART 1 TIME WAITS FOR NO ONE 1 Understanding time series forecasting 2 A naive prediction of the future 3 Going on a random walk PART 2 FORECASTING WITH STATISTICAL MODELS 4 Modeling a moving average process 5 Modeling an autoregressive process 6 Modeling complex time series 7 Forecasting non-stationary time series 8 Accounting for seasonality 9 Adding external variables to our model 10 Forecasting multiple time series 11 Capstone: Forecasting the number of antidiabetic drug prescriptions in Australia PART 3 LARGE-SCALE FORECASTING WITH DEEP LEARNING 12 Introducing deep learning for time series forecasting 13 Data windowing and creating baselines for deep learning 14 Baby steps with deep learning 15 Remembering the past with LSTM 16 Filtering a time series with CNN 17 Using predictions to make more predictions 18 Capstone: Forecasting the electric power consumption of a household PART 4 AUTOMATING FORECASTING AT SCALE 19 Automating time series forecasting with Prophet 20 Capstone: Forecasting the monthly average retail price of steak in Canada 21 Going above and beyond
Time Series Forecasting Using Generative AI: Leveraging AI for Precision Forecasting
by Banglore Vijay Vishwas Sri Ram Macharla"Time Series Forecasting Using Generative AI introduces readers to Generative Artificial Intelligence (Gen AI) in time series analysis, offering an essential exploration of cutting-edge forecasting methodologies." The book covers a wide range of topics, starting with an overview of Generative AI, where readers gain insights into the history and fundamentals of Gen AI with a brief introduction to large language models. The subsequent chapter explains practical applications, guiding readers through the implementation of diverse neural network architectures for time series analysis such as Multi-Layer Perceptrons (MLP), WaveNet, Temporal Convolutional Network (TCN), Bidirectional Temporal Convolutional Network (BiTCN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Deep AutoRegressive(DeepAR), and Neural Basis Expansion Analysis(NBEATS) using modern tools. Building on this foundation, the book introduces the power of Transformer architecture, exploring its variants such as Vanilla Transformers, Inverted Transformer (iTransformer), DLinear, NLinear, and Patch Time Series Transformer (PatchTST). Finally, The book delves into foundation models such as Time-LLM, Chronos, TimeGPT, Moirai, and TimesFM enabling readers to implement sophisticated forecasting models tailored to their specific needs. This book empowers readers with the knowledge and skills needed to leverage Gen AI for accurate and efficient time series forecasting. By providing a detailed exploration of advanced forecasting models and methodologies, this book enables practitioners to make informed decisions and drive business growth through data-driven insights. ● Understand the core history and applications of Gen AI and its potential to revolutionize time series forecasting. ● Learn to implement different neural network architectures such as MLP, WaveNet, TCN, BiTCN, RNN, LSTM, DeepAR, and NBEATS for time series forecasting. ● Discover the potential of Transformer architecture and its variants, such as Vanilla Transformers, iTransformer, DLinear, NLinear, and PatchTST, for time series forecasting. <span sty
Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence (Springer Series on Bio- and Neurosystems #7)
by Nikola K. KasabovSpiking neural networks (SNN) are biologically inspired computational models that represent and process information internally as trains of spikes. This monograph book presents the classical theory and applications of SNN, including original author’s contribution to the area. The book introduces for the first time not only deep learning and deep knowledge representation in the human brain and in brain-inspired SNN, but takes that further to develop new types of AI systems, called in the book brain-inspired AI (BI-AI). BI-AI systems are illustrated on: cognitive brain data, including EEG, fMRI and DTI; audio-visual data; brain-computer interfaces; personalized modelling in bio-neuroinformatics; multisensory streaming data modelling in finance, environment and ecology; data compression; neuromorphic hardware implementation. Future directions, such as the integration of multiple modalities, such as quantum-, molecular- and brain information processing, is presented in the last chapter. The book is a research book for postgraduate students, researchers and practitioners across wider areas, including computer and information sciences, engineering, applied mathematics, bio- and neurosciences.
Time-Triggered Communication (Embedded Systems)
by Roman ObermaisserTime-Triggered Communication helps readers build an understanding of the conceptual foundation, operation, and application of time-triggered communication, which is widely used for embedded systems in a diverse range of industries. This book assembles contributions from experts that examine the differences and commonalities of the most significant protocols including: TTP, FlexRay, TTEthernet, SAFEbus, TTCAN, and LIN. Covering the spectrum, from low-cost time-triggered fieldbus networks to ultra-reliable time-triggered networks used for safety-critical applications, the authors illustrate the inherent benefits of time-triggered communication in terms of predictability, complexity management, fault-tolerance, and analytical dependability modeling, which are key aspects of safety-critical systems. Examples covered include FlexRay in cars, TTP in railway and avionic systems, and TTEthernet in aerospace applications. Illustrating key concepts based on real-world industrial applications, this book: Details the underlying concepts and principles of time-triggered communication Explores the properties of a time-triggered communication system, contrasting its strengths and weaknesses Focuses on the core algorithms applied in many systems, including those used for clock synchronization, startup, membership, and fault isolation Describes the protocols that incorporate presented algorithms Covers tooling requirements and solutions for system integration, including scheduling The information in this book is extremely useful to industry leaders who design and manufacture products with distributed embedded systems based on time-triggered communication. It also benefits suppliers of embedded components or development tools used in this area. As an educational tool, this material can be used to teach students and working professionals in areas including embedded systems, computer networks, system architectures, dependability, real-time systems, and automotive, avionics, and industrial control systems.
Timing for Animation
by Tom SitoThe classic work on animation principles, now fully updated for the digital age.
Timing for Animation, 40th Anniversary Edition
by Harold Whitaker John HalasTiming for Animation has been one of the pillars of animation since it was first published in 1981. Now this 40th anniversary edition captures the focus of the original and enhances this new edition with fresh images, techniques, and advice from world-renowned animators. Not only does the text explore timing in traditional animation, but also timing in digital works. Vibrant illustrations and clear directions line the pages to help depict the various methods and procedures to bring your animation to life. Examples include timing for digital production, digital storyboarding in 2D, digital storyboarding in 3D, and the use of After Effects, as well as interactive games, television, animals, and more. Learn how animated scenes should be arranged in relation to each other, how much space should be used, and how long each drawing should be shown for maximum dramatic effect. All you need to breathe life into your animation is at your fingertips with Timing for Animation. Key Features: Fully revised and updated with modern examples and techniques Explores the fundamentals of timing, physics, and animation Perfect for the animation novice and the expert Get straight to the good stuff with simple, no-nonsense instruction on the key techniques like stretch and squash, animated cycles, overlapping, and anticipation. Trying to time weight, mood, and power can make or break an animation—get it right the first time with these tried and tested techniques. Authors Harold Whitaker was a BAFTA-nominated professional animator and educator for 40 years; many of his students number among today’s most outstanding animation artists. John Halas, known as "The father of British animation" and formerly of Halas & Batchelor Animation Studio, produced more than 2,000 animation films, including the legendary Animal Farm (1954) and the award-winning Dilemma (1981). He was also the founder and president of the International Animated Film Association (ASIFA) and former Chairman of the British Federation of Film Societies. Tom Sito is Professor of Animation at the University of Southern California and has written numerous books and articles on animation. Tom’s screen credits include Shrek (2001) and the Disney classics Who Framed Roger Rabbit (1988), The Little Mermaid (1989), Beauty and the Beast (1991), Aladdin (1992), and The Lion King (1994). In 1998, Tom was named by Animation Magazine as one of the 100 Most Important People in Animation.
Timing Jitter in Time-of-Flight Range Imaging Cameras
by Gehan AnthonysThis book explains how depth measurements from the Time-of-Flight (ToF) range imaging cameras are influenced by the electronic timing-jitter. The author presents jitter extraction and measurement techniques for any type of ToF range imaging cameras. The author mainly focuses on ToF cameras that are based on the amplitude modulated continuous wave (AMCW) lidar techniques that measure the phase difference between the emitted and reflected light signals. The book discusses timing-jitter in the emitted light signal, which is sensible since the light signal of the camera is relatively straightforward to access. The specific types of jitter that present on the light source signal are investigated throughout the book. The book is structured across three main sections: a brief literature review, jitter measurement, and jitter influence in AMCW ToF range imaging.
Timing Performance of Nanometer Digital Circuits Under Process Variations (Frontiers In Electronic Testing Ser. #39)
by Jose Garcia Gervacio Victor ChampacThis book discusses the digital design of integrated circuits under process variations, with a focus on design-time solutions. The authors describe a step-by-step methodology, going from logic gates to logic paths to the circuit level. Topics are presented in comprehensively, without overwhelming use of analytical formulations. Emphasis is placed on providing digital designers with understanding of the sources of process variations, their impact on circuit performance and tools for improving their designs to comply with product specifications. Various circuit-level “design hints” are highlighted, so that readers can use then to improve their designs. A special treatment is devoted to unique design issues and the impact of process variations on the performance of FinFET based circuits. This book enables readers to make optimal decisions at design time, toward more efficient circuits, with better yield and higher reliability.
Timmy's Monster Diary: Screen Time Stress (But I Tame It, Big Time) (Monster Diaries #2)
by Raun Melmed Annette SextonMeet Timmy, a lovable monster who can’t get enough of the coolest gadgets and video games. Too bad he doesn’t realize how much time he spends each day in front of a screen. In the same humorous spirit of Diary of a Wimpy Kid comes Timmy’s Monster Diary: Screen Time Stress. Using the “Time-Telling” and “ST4” techniques developed by Dr. Raun Melmed of the Melmed Center in Arizona, Timmy’s Monster Diary teaches kids how to self-monitor the amount of time they spend on technology. Timmy’s hilarious doodles and diary entries chronicle his delightful adventures, misadventures, and eventual triumph in a funny, relatable way. It’s the one book that kids will want to turn off the TV and read! Timmy’s Monster Diary also includes a resource section to help parents and teachers implement Dr. Melmed’s methods, plus ST4 reminders that kids can remove, color, and place around the house. Ages 6–12 Don’t miss Marvin’s ADHD adventures in Book 1.
Tiny Android Projects Using Kotlin
by Denis Panjuta Loveth NwokikeIn today’s fast-paced world, Android development is a rapidly evolving field that requires regular updates to keep up with the latest trends and technologies. Tiny Android Projects Using Kotlin is an excellent resource for developers who want to learn to build Android applications using the latest tools and frameworks. KEY FEATURES • Teaches building Android apps using Kotlin, XML, and Jetpack Compose • Includes saving data on the device using the Room database library • Teaches communication between an Android device and data on the internet using REST API • Shows how to create different Android menu navigations using Jetpack Compose • Introduces the most architectures used in Android Projects and implements MVVM With Kotlin being the most preferred language for Android development, this book provides a practical, hands-on approach to learning the language and building high-quality Android apps using Kotlin, XML, and Jetpack Compose.
Tiny C Projects
by Dan GookinLearn the big skills of C programming by creating bite-size projects! Work your way through these 15 fun and interesting tiny challenges to master essential C techniques you&’ll use in full-size applications.In Tiny C Projects you will learn how to: Create libraries of functions for handy use and re-use Process input through an I/O filter to generate customized output Use recursion to explore a directory tree and find duplicate files Develop AI for playing simple games Explore programming capabilities beyond the standard C library functions Evaluate and grow the potential of your programs Improve code to better serve users Tiny C Projects is an engaging collection of 15 small programming challenges! This fun read develops your C abilities with lighthearted games like tic-tac-toe, utilities like a useful calendar, and thought-provoking exercises like encoding and cyphers. Jokes and lighthearted humor make even complex ideas fun to learn. Each project is small enough to complete in a weekend, and encourages you to evolve your code, add new functions, and explore the full capabilities of C. About the technology The best way to gain programming skills is through hands-on projects—this book offers 15 of them. C is required knowledge for systems engineers, game developers, and roboticists, and you can start writing your own C programs today. Carefully selected projects cover all the core coding skills, including storing and modifying text, reading and writing files, searching your computer&’s directory system, and much more. About the book Tiny C Projects teaches C gradually, from project to project. Covering a variety of interesting cases, from timesaving tools, simple games, directory utilities, and more, each program you write starts out simple and gets more interesting as you add features. Watch your tiny projects grow into real applications and improve your C skills, step by step. What's inside Caesar cipher solver: Use an I/O filter to generate customized output Duplicate file finder: Use recursion to explore a directory tree Daily greetings: Writing the moon phase algorithm Lotto pics: Working with random numbers And 11 more fun projects! About the reader For C programmers of all skill levels. About the author Dan Gookin has over 30 years of experience writing about complex topics. His most famous work is DOS For Dummies, which established the entire For Dummies brand. Table of Contents 1 Configuration and setup 2 Daily greetings 3 NATO output 4 Caesarean cipher 5 Encoding and decoding 6 Password generators 7 String utilities 8 Unicode and wide characters 9 Hex dumper 10 Directory tree 11 File finder 12 Holiday detector 13 Calendar 14 Lotto picks 15 Tic-tac-toe
Tiny CSS Projects
by Martine Dowden Michael GearonCSS is a must-know language for all web developers. In this practical book, you&’ll explore numerous techniques to improve the way you write CSS as you build 12 tiny projects.In Tiny CSS Projects you&’ll build twelve exciting and useful web projects including: A loading screen created by styling SVG graphics A responsive newspaper layout with multiple columns Animating social media buttons with pseudo-elements Designing layouts using CSS grids Summary cards that utilize hover interactions Styling forms to make them more appealing to your users The projects may be tiny, but the CSS skills you&’ll learn are huge! Tiny CSS Projects teaches you how to make beautiful websites and applications by guiding you through a dozen fun coding challenges. You&’ll learn important skills through hands-on practice as you tinker with your own code and make real creative decisions about the projects you&’re building. You&’ll rapidly master the basics and then press on into CSS&’s exciting layout features including grid and flexbox, animations, transitions, and media queries. About the Technology Don&’t settle for boring web pages! With Cascading Style Sheets you can control color, layout, and typography to make your sites both functional and beautiful. CSS is a essential skill for web developers and designers. This book will help you get started the right way. About the Book Tiny CSS Projects builds your CSS skills by guiding you through 12 creative mini-projects. Each interesting challenge starts with a downloadable HTML skeleton. As you flesh it out with your own design ideas, you&’ll master CSS concepts like transitions, layout, and styling forms, and explore powerful features including Flexbox and Grid. All the skills you&’ll learn are easy to transfer to full-size applications. When you finish, you&’ll have an exciting portfolio of designs ready to go for your next project. What's Inside Transitions and animations using keyframes Layout techniques including Grid and Flexbox Styling form elements including radio buttons Embedding fonts and typography-related styles Conditional styling using pseudo-elements and media queries About the reader For readers who know the basics of HTML and frontend development. No previous experience with CSS is required. About the author Martine Dowden is an author, speaker, and award-winning CTO. Michael Gearon is a user experience designer and frontend developer who has worked with many well-known brands. Table of Contents 1 CSS introduction 2 Designing a layout using CSS Grid 3 Creating a responsive animated loading screen 4 Creating a responsive web newspaper layout 5 Summary cards with hover interactions 6 Creating a profile card 7 Harnessing the full power of float 8 Designing a checkout cart 9 Creating a virtual credit card 10 Styling forms 11 Animated social media share links 12 Using preprocessors
A Tiny Handbook of R (SpringerBriefs in Statistics)
by Mike AllerhandThis Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.
Tiny Machine Learning Quickstart: Machine Learning for Arduino Microcontrollers (Maker Innovations Series)
by Simone SalernoBe a part of the Tiny Machine Learning (TinyML) revolution in the ever-growing world of IoT. This book examines the concepts, workflows, and tools needed to make your projects smarter, all within the Arduino platform. You’ll start by exploring Machine learning in the context of embedded, resource-constrained devices as opposed to your powerful, gigabyte-RAM computer. You’ll review the unique challenges it poses, but also the limitless possibilities it opens. Next, you’ll work through nine projects that encompass different data types (tabular, time series, audio and images) and tasks (classification and regression). Each project comes with tips and tricks to collect, load, plot and analyse each type of data. Throughout the book, you’ll apply three different approaches to TinyML: traditional algorithms (Decision Tree, Logistic Regression, SVM), Edge Impulse (a no-code online tools), and TensorFlow for Microcontrollers. Each has its strengths and weaknesses, and you will learn how to choose the most appropriate for your use case. TinyML Quickstart will provide a solid reference for all your future projects with minimal cost and effort. What You Will Learn Navigate embedded ML challenges Integrate Python with Arduino for seamless data processing Implement ML algorithms Harness the power of Tensorflow for artificial neural networks Leverage no-code tools like Edge Impulse Execute real-world projects Who This Book Is For Electronics hobbyists and developers with a basic understanding of Tensorflow, ML in Python, and Arduino-based programming looking to apply that knowledge with microcontrollers. Previous experience with C++ is helpful but not required.
Tiny Python Projects: Learn coding and testing with puzzles and games
by Ken Youens-Clark&”Tiny Python Projects is a gentle and amusing introduction to Python that will firm up key programming concepts while also making you giggle.&”—Amanda Debler, Schaeffler Key Features Learn new programming concepts through 21-bitesize programs Build an insult generator, a Tic-Tac-Toe AI, a talk-like-a-pirate program, and more Discover testing techniques that will make you a better programmer Code-along with free accompanying videos on YouTube Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.About The Book The 21 fun-but-powerful activities in Tiny Python Projects teach Python fundamentals through puzzles and games. You&’ll be engaged and entertained with every exercise, as you learn about text manipulation, basic algorithms, and lists and dictionaries, and other foundational programming skills. Gain confidence and experience while you create each satisfying project. Instead of going quickly through a wide range of concepts, this book concentrates on the most useful skills, like text manipulation, data structures, collections, and program logic with projects that include a password creator, a word rhymer, and a Shakespearean insult generator. Author Ken Youens-Clark also teaches you good programming practice, including writing tests for your code as you go. What You Will Learn Write command-line Python programs Manipulate Python data structures Use and control randomness Write and run tests for programs and functions Download testing suites for each project This Book Is Written For For readers familiar with the basics of Python programming. About The Author Ken Youens-Clark is a Senior Scientific Programmer at the University of Arizona. He has an MS in Biosystems Engineering and has been programming for over 20 years. Table of Contents 1 How to write and test a Python program 2 The crow&’s nest: Working with strings 3 Going on a picnic: Working with lists 4 Jump the Five: Working with dictionaries 5 Howler: Working with files and STDOUT 6 Words count: Reading files and STDIN, iterating lists, formatting strings 7 Gashlycrumb: Looking items up in a dictionary 8 Apples and Bananas: Find and replace 9 Dial-a-Curse: Generating random insults from lists of words 10 Telephone: Randomly mutating strings 11 Bottles of Beer Song: Writing and testing functions 12 Ransom: Randomly capitalizing text 13 Twelve Days of Christmas: Algorithm design 14 Rhymer: Using regular expressions to create rhyming words 15 The Kentucky Friar: More regular expressions 16 The Scrambler: Randomly reordering the middles of words 17 Mad Libs: Using regular expressions 18 Gematria: Numeric encoding of text using ASCII values 19 Workout of the Day: Parsing CSV files, creating text table output 20 Password strength: Generating a secure and memorable password 21 Tic-Tac-Toe: Exploring state 22 Tic-Tac-Toe redux: An interactive version with type hints
Tiny Worlds
by Charles NeedleSuccessful nature photographer and lecturer Charles Needle often asks students in his workshops if they understand the difference between "looking" and "seeing". The difference he is pointing out is that while we are constantly "looking" at countless people, places and things, we might not be actually seeing what is right in front of us. Being tuned into this difference can elevate your art as a photographer, allowing you to be more in tune with your surroundings. Needle has applied this concept to his work and it shows. Utilizing macro photography to capture the nuances of the nature all around us. In this extensive handbook, Needle covers not only his philosophy for seeing and capturing nature photography but delves into the equipment he uses. Needle covers composition, flash techniques, the fundamentals of macro photography and so much more. The book features many set-up shots, equipment shots and sequential shots detailing the progression towards creating the final images. This essential text provides both the inspiration and the technique required to beautifully capture the wonders of nature.
TinyML: Machine Learning with TensorFlow Lite on Arduino and Ultra-Low-Power Microcontrollers
by Pete Warden Daniel SitunayakeDeep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices.Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary.Build a speech recognizer, a camera that detects people, and a magic wand that responds to gesturesWork with Arduino and ultra-low-power microcontrollersLearn the essentials of ML and how to train your own modelsTrain models to understand audio, image, and accelerometer dataExplore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyMLDebug applications and provide safeguards for privacy and securityOptimize latency, energy usage, and model and binary size
TinyML Cookbook: Combine machine learning with microcontrollers to solve real-world problems
by Gian Marco IodiceOver 70 recipes to help you develop smart applications on Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano using the power of machine learning Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesOver 20+ new recipes, including recognizing music genres and detecting objects in a sceneCreate practical examples using TensorFlow Lite for Microcontrollers, Edge Impulse, and moreExplore cutting-edge technologies, such as on-device training for updating models without data leaving the deviceBook DescriptionDiscover the incredible world of tiny Machine Learning (tinyML) and create smart projects using real-world data sensors with the Arduino Nano 33 BLE Sense, Raspberry Pi Pico, and SparkFun RedBoard Artemis Nano. TinyML Cookbook, Second Edition, will show you how to build unique end-to-end ML applications using temperature, humidity, vision, audio, and accelerometer sensors in different scenarios. These projects will equip you with the knowledge and skills to bring intelligence to microcontrollers. You'll train custom models from weather prediction to real-time speech recognition using TensorFlow and Edge Impulse.Expert tips will help you squeeze ML models into tight memory budgets and accelerate performance using CMSIS-DSP. This improved edition includes new recipes featuring an LSTM neural network to recognize music genres and the Faster-Objects-More-Objects (FOMO) algorithm for detecting objects in a scene. Furthermore, you’ll work on scikit-learn model deployment on microcontrollers, implement on-device training, and deploy a model using microTVM, including on a microNPU. This beginner-friendly and comprehensive book will help you stay up to date with the latest developments in the tinyML community and give you the knowledge to build unique projects with microcontrollers!What you will learnUnderstand the microcontroller programming fundamentalsWork with real-world sensors, such as the microphone, camera, and accelerometerImplement an app that responds to human voice or recognizes music genresLeverage transfer learning with FOMO and KerasLearn best practices on how to use the CMSIS-DSP libraryCreate a gesture-recognition app to build a remote controlDesign a CIFAR-10 model for memory-constrained microcontrollersTrain a neural network on microcontrollersWho this book is forThis book is ideal for machine learning engineers or data scientists looking to build embedded/edge ML applications and IoT developers who want to add machine learning capabilities to their devices. If you’re an engineer, student, or hobbyist interested in exploring tinyML, then this book is your perfect companion. Basic familiarity with C/C++ and Python programming is a prerequisite; however, no prior knowledge of microcontrollers is necessary to get started with this book.
TinyML Cookbook: Combine artificial intelligence and ultra-low-power embedded devices to make the world smarter
by Gian Marco Iodice Ronan NaughtonWork through over 50 recipes to develop smart applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico using the power of machine learningKey FeaturesTrain and deploy ML models on Arduino Nano 33 BLE Sense and Raspberry Pi PicoWork with different ML frameworks such as TensorFlow Lite for Microcontrollers and Edge ImpulseExplore cutting-edge technologies such as microTVM and Arm Ethos-U55 microNPUBook DescriptionThis book explores TinyML, a fast-growing field at the unique intersection of machine learning and embedded systems to make AI ubiquitous with extremely low-powered devices such as microcontrollers. The TinyML Cookbook starts with a practical introduction to this multidisciplinary field to get you up to speed with some of the fundamentals for deploying intelligent applications on Arduino Nano 33 BLE Sense and Raspberry Pi Pico. As you progress, you'll tackle various problems that you may encounter while prototyping microcontrollers, such as controlling the LED state with GPIO and a push-button, supplying power to microcontrollers with batteries, and more. Next, you'll cover recipes relating to temperature, humidity, and the three “V” sensors (Voice, Vision, and Vibration) to gain the necessary skills to implement end-to-end smart applications in different scenarios. Later, you'll learn best practices for building tiny models for memory-constrained microcontrollers. Finally, you'll explore two of the most recent technologies, microTVM and microNPU that will help you step up your TinyML game. By the end of this book, you'll be well-versed with best practices and machine learning frameworks to develop ML apps easily on microcontrollers and have a clear understanding of the key aspects to consider during the development phase.What you will learnUnderstand the relevant microcontroller programming fundamentalsWork with real-world sensors such as the microphone, camera, and accelerometerRun on-device machine learning with TensorFlow Lite for MicrocontrollersImplement an app that responds to human voice with Edge ImpulseLeverage transfer learning to classify indoor rooms with Arduino Nano 33 BLE SenseCreate a gesture-recognition app with Raspberry Pi PicoDesign a CIFAR-10 model for memory-constrained microcontrollersRun an image classifier on a virtual Arm Ethos-U55 microNPU with microTVMWho this book is forThis book is for machine learning developers/engineers interested in developing machine learning applications on microcontrollers through practical examples quickly. Basic familiarity with C/C++, the Python programming language, and the command-line interface (CLI) is required. However, no prior knowledge of microcontrollers is necessary.