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Programming Machine Learning : From Coding to Deep Learning
You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start from the beginning and code your way to machine learning mastery.What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly.Apart from that, you'll only need your computer, and your code-adept brain.
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Hands-on Rust : Effective Learning through 2D Game Development and Play
Rust is an exciting new programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters - and what better way to learn than by making games.Each chapter in this book presents hands-on, practical projects ranging from "Hello, World" to building a full dungeon crawler game.With this book, you'll learn game development skills applicable to other engines, including Unity and Unreal. Rust is an exciting programming language combining the power of C with memory safety, fearless concurrency, and productivity boosters.With Rust, you have a shiny new playground where your game ideas can flourish. Each chapter in this book presents hands-on, practical projects that take you on a journey from "Hello, World" to building a full dungeon crawler game.Start by setting up Rust and getting comfortable with your development environment.Learn the language basics with practical examples as you make your own version of Flappy Bird.Discover what it takes to randomly generate dungeons and populate them with monsters as you build a complete dungeon crawl game.Run game systems concurrently for high-performance and fast game-play, while retaining the ability to debug your program.Unleash your creativity with magical items, tougher monsters, and intricate dungeon design.Add layered graphics and polish your game with style. What You Need:A computer running Windows 10, Linux, or Mac OS X. A text editor, such as Visual Studio Code. A video card and drivers capable of running OpenGL 3.2.
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Hands-On Automated Machine Learning
Automate data and model pipelines for faster machine learning applicationsAbout This Book• Build automated modules for different machine learning components• Understand each component of a machine learning pipeline in depth• Learn to use different open source AutoML and feature engineering platformsWho This Book Is ForIf you're a budding data scientist, data analyst, or Machine Learning enthusiast and are new to the concept of automated machine learning, this book is ideal for you.You'll also find this book useful if you're an ML engineer or data professional interested in developing quick machine learning pipelines for your projects.Prior exposure to Python programming will help you get the best out of this book. What You Will Learn• Understand the fundamentals of Automated Machine Learning systems• Explore auto-sklearn and MLBox for AutoML tasks • Automate your preprocessing methods along with feature transformation• Enhance feature selection and generation using the Python stack• Assemble individual components of ML into a complete AutoML framework• Demystify hyperparameter tuning to optimize your ML models• Dive into Machine Learning concepts such as neural networks and autoencoders • Understand the information costs and trade-offs associated with AutoMLIn DetailAutoML is designed to automate parts of Machine Learning.Readily available AutoML tools are making data science practitioners' work easy and are received well in the advanced analytics community.Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible.In this book, you'll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more.In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning.By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you'll be able to incorporate automation tasks using practical datasets.You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions. Style and approachStep by step approach to understand how to automate your machine learning tasks
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Hands-on Intermediate Econometrics Using R: Templates For Learning Quantitative Methods And R Software
How to learn both applied statistics (econometrics) and free, open-source software R?This book allows students to have a sense of accomplishment by copying and pasting many hands-on templates provided here.The textbook is essential for anyone wishing to have a practical understanding of an extensive range of topics in Econometrics.No other text provides software snippets to learn so many new statistical tools with hands-on examples.The explicit knowledge of inputs and outputs of each new method allows the student to know which algorithm is worth studying.The book offers sufficient theoretical and algorithmic details about a vast range of statistical techniques.The second edition's preface lists the following topics generally absent in other textbooks. (i) Iteratively reweighted least squares, (ii) Pillar charts to represent 3D data. (iii) Stochastic frontier analysis (SFA) (iv) model selection with Mallows' Cp criterion. (v) Hodrick-Prescott (HP) filter. (vi) Automatic ARIMA models. (vi) Nonlinear Granger-causality using kernel regressions and bootstrap confidence intervals. (vii) new Keynesian Phillips curve (NKPC). (viii) Market-neutral pairs trading using two cointegrated stocks. (ix) Artificial neural network (ANN) for product-specific forecasting. (x) Vector AR and VARMA models. (xi) New tools for diagnosing the endogeneity problem. (xii) The elegant set-up of k-class estimators and identification. (xiii) Probit-logit models and Heckman selection bias correction. (xiv) Receiver operating characteristic (ROC) curves and areas under them. (xv) Confusion matrix. (xvi) Quantile regression (xvii) Elastic net estimator. (xviii) generalized Correlations (xix) maximum entropy bootstrap for time series. (xx) Convergence concepts quantified. (xxi) Generalized partial correlation coefficients (xxii) Panel data and duration (survival) models.
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Is learning programming and software development very challenging?
Learning programming and software development can be challenging for some people, as it requires logical thinking, problem-solving skills, and attention to detail. However, with dedication, practice, and the right resources, it is definitely achievable. Breaking down complex concepts into smaller, more manageable parts and seeking help from online tutorials, courses, and communities can make the learning process easier and more enjoyable. Ultimately, the level of challenge will vary depending on the individual's background, experience, and learning style.
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Which programming languages are used in software development?
There are many programming languages used in software development, including popular languages such as Java, Python, C++, JavaScript, and Ruby. Each language has its own strengths and is used for different purposes in software development. For example, Java is commonly used for building enterprise-level applications, while Python is known for its simplicity and versatility. C++ is often used for system software and game development, while JavaScript is essential for web development. Overall, the choice of programming language depends on the specific requirements of the software being developed.
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What is the difference between software development and programming?
Software development is a broader term that encompasses the entire process of creating software, including planning, designing, testing, and maintaining software applications. Programming, on the other hand, refers specifically to the act of writing code to instruct a computer to perform certain tasks. While programming is a key component of software development, software development involves a more comprehensive approach that includes various stages beyond just writing code.
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Are hands prepared for holding hands?
Yes, hands are naturally prepared for holding hands. The human hand is designed with fingers that can interlock with another person's fingers, providing a secure and comfortable grip. Additionally, the skin on our hands contains touch receptors that enhance the sensation of holding hands, making it a pleasurable and intimate experience. Overall, the structure and sensitivity of our hands make them well-suited for holding hands with others.
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Sensory Toy for Fine Motor Skills W/ Hands,Shape&Sorting , Learning Educational Early Development
Sensory Toy for Fine Motor Skills W/ Hands,Shape&Sorting , Learning Educational Early Development
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Hands-On Machine Learning with R
Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today’s most popular machine learning methods.This book serves as a practitioner’s guide to the machine learning process and is meant to help the reader learn to apply the machine learning stack within R, which includes using various R packages such as glmnet, h2o, ranger, xgboost, keras, and others to effectively model and gain insight from their data.The book favors a hands-on approach, providing an intuitive understanding of machine learning concepts through concrete examples and just a little bit of theory.Throughout this book, the reader will be exposed to the entire machine learning process including feature engineering, resampling, hyperparameter tuning, model evaluation, and interpretation.The reader will be exposed to powerful algorithms such as regularized regression, random forests, gradient boosting machines, deep learning, generalized low rank models, and more!By favoring a hands-on approach and using real word data, the reader will gain an intuitive understanding of the architectures and engines that drive these algorithms and packages, understand when and how to tune the various hyperparameters, and be able to interpret model results.By the end of this book, the reader should have a firm grasp of R’s machine learning stack and be able to implement a systematic approach for producing high quality modeling results. Features:· Offers a practical and applied introduction to the most popular machine learning methods. · Topics covered include feature engineering, resampling, deep learning and more. · Uses a hands-on approach and real world data.
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Learning, Training, and Development in Organizations
This scholarly book in SIOP’s Organizational Frontier series looks at research on enhancing knowledge acquisition and its application in organizations.It concentrates on training, design and delivery given the changing nature of work and organizations.Now that work is increasingly complex, there is greater emphasis on expertise and cognitive skills.Advances in technology such as computer simulations and web-based training are necessitating a more active role for the learner in the training process.In the broad context of the organization systems, this book promotes learning and development as a continuous lifelong endeavor.
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Effective Software Development for the Enterprise : Beyond Domain Driven Design, Software Architecture, and Extreme Programming
It’s a jungle out there. Delivering software solutions with predictable costs, time, scope, and quality has become more complicated than ever.Developers need to account for multiple moving targets, including often avoided but always relevant factors such as culture, people, and leadership.To effectively deal with these challenges, it’s necessary to rethink the entire delivery chain of modern software development.This book addresses all these topics, putting everything in perspective, from the potential awkwardness created by cultural differences to connecting code with business outcomes to make delivering quality solutions possible.As a software engineer who strives to be his best and challenge the status quo when building IT systems, author Tengiz Tutisani always wanted a book that would cover all of these elements from a developer’s perspective — so he wrote one.Effective Software Development for the Enterprise builds on advanced disciplines, methodologies, and techniques derived from domain-driven design, software architecture, and extreme programming.Its objective is to aid modern software engineers and solution architects in building and delivering high-quality solutions for an enterprise while meeting ambitious criteria:Meet users' expectationsDeliver solutions on time with no defectsCreate products that can scale-out horizontallySolutions should not require a dedicated production support teamAccelerate development paceDouble ROI per developer, team, and softwareYou will learn how to achieve these objectives in any engineering environment by developing solid strategies based on the industry's well-known, recognized, and proven patterns and principles.After reading this book, you will be ready to effectively deal with any and all challenges posed by today’s hyper-competitive, globally-diverse, fast-moving enterprise environment. Who This Book Is ForProfessional software engineers and solution architects, as well as those aspiring to become one.Additionally, software engineering leaders will benefit from reading it, as it will help them maximize their impact via successful software project deliveries.
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What training opportunities are available for IT specialists to specialize in web development and general programming, such as software development?
There are various training opportunities available for IT specialists looking to specialize in web development and general programming. Online platforms like Coursera, Udemy, and Codecademy offer courses and certifications in programming languages such as Python, Java, and JavaScript. Additionally, coding bootcamps like General Assembly and Flatiron School provide intensive, hands-on training in software development. IT specialists can also pursue advanced degrees in computer science or related fields to deepen their knowledge and skills in web development and programming.
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What training options are available for IT specialists to specialize in web development and general programming, such as software development?
IT specialists looking to specialize in web development and general programming have a variety of training options available to them. They can pursue formal education through degree programs in computer science or related fields, attend coding bootcamps that offer intensive training in programming languages and frameworks, or take online courses and tutorials to learn specific skills. Additionally, IT specialists can participate in workshops, seminars, and conferences to stay updated on the latest trends and technologies in web development and software development. Continuous learning and practice are essential for IT specialists to excel in these specialized areas.
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Do hands get bigger through strength training?
Strength training can increase muscle mass and overall size of the hands due to muscle growth. However, the actual size of the bones in the hands will not change significantly through strength training. The increase in muscle size can make the hands appear larger and more defined, but the bones themselves will not grow in size.
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Why do my hands tingle during training?
Tingling in the hands during training can be caused by a few different factors. One common reason is that the nerves in your hands are being compressed or irritated, which can happen when you're gripping weights or using certain equipment. This can lead to a sensation of tingling or numbness. It's also possible that you're putting pressure on a nerve in your neck or shoulder, which can cause tingling to radiate down into your hands. If the tingling is persistent or severe, it's a good idea to consult with a healthcare professional to rule out any underlying issues.
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