Garrett hawthorne (17 risultati)

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condizione: Brand New. 168 pages. 9.00x6.00x0.42 inches. In Stock. Maguire, Matthew; Schoenherr, John; Dowgin, Christopher Jon Luke (illustratore).

Hardcover. Condizione: Good. 1st Edition, Limited Edition. This is a two volume set, complete, of this title each volume is 6 1/4 x 9 3/4 x 2 1/4 inches. There is a frontispiece and three additional etchings in each volume. Volume 1 of this large paper edition has 505 pages, Volume 2 has 465 pages-with some pages in the Index uncut at the top. Both volumes are #18 of an edition of 350. The edges show the boards on both volumes, all spine ends show wear, and the paper label on volume one has a significant amount of paper loss. All four gutters are sound. Each volume has a cancelled/discard statement on a University bookplate affixed to the front paste down. The edges of the leaves are toned but the interior is clean and bright. The thin paper separating the frontispiece of Volume 1 is missing, but both volumes show toning to the title page. Please look at the pictures carefully, and ask questions about these volumes prior to making a purchase-thank you. The shipment of this set will require additional charges than are typically applied to orders. Etchings by S. A. Schoff and E. H. Garrett (illustratore).…

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. "I don't know R" shouldn't be the thing standing between you and your research, your job, or your degree. This book fixes that. Most R books are written for statisticians who already think in formulas and matrix notation, opening with terms like "vectorized operation" before showing a single real, runnable line of code. Data Science with R takes the opposite approach: every concept is introduced because you'll actually use it, illustrated with real, working code that produces a genuine result you can see immediately. This is a true data science book built around R as it's actually used in 2026 - the tidyverse, ggplot2, and real statistical modeling - not an abstract statistics textbook that happens to use R as its calculator. What You'll Actually LearnR fundamentals - vectors, data types, control flow, and functionsData frames, R's core data structure, in genuine depthData cleaning with dplyr and reshaping with tidyrString manipulation and working with datesData visualization with ggplot2 - two full chapters, building real skill, not just syntaxDescriptive statistics, probability distributions, and hypothesis testingLinear regression modeling in RR Markdown for genuine, shareable, professional reportingA complete capstone - a real R data analysis project, start to finish Why This Book Is DifferentNo prior R experience assumed - if you can build a spreadsheet formula, you can start here.Genuinely for beginners at statistics too - Chapters 12 through 14 teach modeling at a true beginner level, not a graduate-statistics one.Pairs naturally with Python if you already know it - understand precisely where and why a real team reaches for R over Python.Full exercise solutions across every chapter. Who This Book Is For Written for complete beginners to R - researchers, public health and economics professionals, social scientists, and students in fields where R is the assigned or expected standard. You're exactly the right reader if every R resource you've tried jumped straight into statistical theory without showing you why it matters, if you want to understand the models and charts in research papers instead of treating them as a black box, or if you already know Python and want a genuine second tool in your kit. This book is not written for readers already fluent in R looking for an advanced statistical reference - it's a genuine, patient, complete on-ramp. What You'll Be Able to Do By the final page, you'll confidently import and clean real data, build publication-quality visualizations with ggplot2, run and interpret hypothesis tests, fit a linear regression model, and produce a polished, shareable analysis report - entirely in R. Scroll up and start your R journey today. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Condizione: New. Print on Demand.

VG+/NONE. THIS IS VOL 20 OF THE "AUTOGRAPH EDITION" (1 OF 500) WITH THE FRONTICEPIECE SIGNED BY EDMUND H. GARRETT AND SCARCE THUS. THE BINDING OF THIS BOOK IS 3/4 GREEN MOROCCAN LEATHER WITH MARBLED PAPER-COVERED BOARDS AND IS NOT MENTIONED IN BAL (THIS IS DIFFERENT THAN THE CREAM COLORED BUCKRAM WITH PRINTED SPINE LABLES USUALLY SEEN WHEN ONE OF THE 500 COPIES OF AUTOGRAPH EDITION IS ENCOUNTERED-POSSIBLY A PUBLISHER'S SPECIAL BINDING) THE BOOK IS VERY NICE, HOWEVER THE GREEN LEATHER IS FADED TO BROWN ON THE SPINE BUT THE GILT TITLES AND DESIGNS ARE BRIGHT. THREE OTHER FULL PAGE ILLUSTRATIONS FROM PHOTOGRAPHS BY CLIFTON JOHNSON. Binding is HARDCOVER.…

Hardcover. Condizione: Very Good. No Jacket (as issued). First Edition. 1903. 1st Printing. Scarce. Nice Tight Clean copy ! Dark green cloth boards, gold and white decor, top edges gold gilt. Printed on one side of leaf only. 64 pages. W. Starling Burgess designed three victorious America's Cup defenders, first American to design twelve-meter class yachts, known for his airplane manufacturing company, which flew the first airplane in New England, created the Dymaxion automobile with Buckminster Fuller, married Rosamond Tudor after her scandalous divorce, father of renown illustrator (Na)Tasha (Starling) Tudor. In 1994, Michael Parker, in the journal Printing History, presented compelling evidence that Burgess deserves designing credit for the world's most popular font, Times New Roman. His one published volume of poetry, The Eternal Laughter and Other Poems, while not popular with the public, received critical acclaim among fellow poets of his day. RBR13.…

Paperback. Condizione: new. Paperback. "I don't know R" shouldn't be the thing standing between you and your research, your job, or your degree. This book fixes that. Most R books are written for statisticians who already think in formulas and matrix notation, opening with terms like "vectorized operation" before showing a single real, runnable line of code. Data Science with R takes the opposite approach: every concept is introduced because you'll actually use it, illustrated with real, working code that produces a genuine result you can see immediately. This is a true data science book built around R as it's actually used in 2026 - the tidyverse, ggplot2, and real statistical modeling - not an abstract statistics textbook that happens to use R as its calculator. What You'll Actually LearnR fundamentals - vectors, data types, control flow, and functionsData frames, R's core data structure, in genuine depthData cleaning with dplyr and reshaping with tidyrString manipulation and working with datesData visualization with ggplot2 - two full chapters, building real skill, not just syntaxDescriptive statistics, probability distributions, and hypothesis testingLinear regression modeling in RR Markdown for genuine, shareable, professional reportingA complete capstone - a real R data analysis project, start to finish Why This Book Is DifferentNo prior R experience assumed - if you can build a spreadsheet formula, you can start here.Genuinely for beginners at statistics too - Chapters 12 through 14 teach modeling at a true beginner level, not a graduate-statistics one.Pairs naturally with Python if you already know it - understand precisely where and why a real team reaches for R over Python.Full exercise solutions across every chapter. Who This Book Is For Written for complete beginners to R - researchers, public health and economics professionals, social scientists, and students in fields where R is the assigned or expected standard. You're exactly the right reader if every R resource you've tried jumped straight into statistical theory without showing you why it matters, if you want to understand the models and charts in research papers instead of treating them as a black box, or if you already know Python and want a genuine second tool in your kit. This book is not written for readers already fluent in R looking for an advanced statistical reference - it's a genuine, patient, complete on-ramp. What You'll Be Able to Do By the final page, you'll confidently import and clean real data, build publication-quality visualizations with ggplot2, run and interpret hypothesis tests, fit a linear regression model, and produce a polished, shareable analysis report - entirely in R. Scroll up and start your R journey today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condizione: new. Paperback. A chart that runs without errors and a chart that actually communicates are two completely different achievements. This book builds the second one. Most visualization tutorials teach a library's syntax - call this function, set that parameter - and stop there, leaving the genuinely important questions unanswered: which chart type actually fits this question, and is your chart honestly representing your data, or accidentally misleading whoever looks at it? Data Visualization with Python opens not with a function call, but with two datasets sharing an identical mean and standard deviation that look completely different once actually plotted - and builds every chapter around that same kind of concrete, honest demonstration. What You'll Actually BuildMatplotlib foundations - figures, axes, and the full plotting pipeline, from precise pixel-level controlEvery essential chart type: bar, line, scatter, and histogram, used correctlyStatistical visualization with Seaborn - distributions, KDE, box plots, and violin plotsCorrelation heatmaps and pair plots that reveal real relationships in dataTime series and categorical data visualization, done rightGenuinely interactive charts and dashboards with PlotlyGeospatial visualizations - real maps from real dataA complete capstone - a full data visualization project, start to finish Why This Book Is DifferentThree complementary tools, learned together - Matplotlib for precise control, Seaborn for statistical convenience, Plotly for genuine interactivity.A full chapter on design principles and visual storytelling - not abstract theory, but immediately tied to a working example.Honesty as a core principle, not an afterthought - you'll learn exactly how charts mislead, and how to make sure yours never do.Full exercise solutions across every chapter. Who This Book Is For Written for readers who've already completed at least one earlier book in this series, or have equivalent working knowledge of Python and pandas DataFrames. No prior visualization or design experience is assumed at all. You're exactly the right reader if you've written basic pandas code and want to actually show your data instead of only printing it as text, if you've built occasional charts but want a genuinely systematic skillset instead of copy-pasted snippets, or if you're preparing for a data role where colleagues expect clear, professional visualizations - not just accurate models. What You'll Be Able to Do By the final page, you'll choose the right chart type for any real question, build statistically rigorous and genuinely interactive visualizations, design dashboards a real audience can explore, and tell an honest, clear, compelling story with your data, every time. Scroll up and start building charts people actually understand. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. …

Paperback. Condizione: new. Paperback. Build a neural network that recognizes images and understands text - starting from a single neuron. Deep learning has a reputation for being impenetrable: the domain of specialists fluent in calculus most readers don't have. Deep Learning for Absolute Beginners proves that reputation wrong. Every idea - a neuron, a layer, backpropagation, a loss function - is built up from plain language and a concrete analogy, then made real with runnable Python and TensorFlow/Keras code, before any formula appears. This isn't a theory book. It's a build-it-yourself book. What You'll Actually BuildYour first working neural network, trained and evaluated in KerasA real image classifier using Convolutional Neural Networks (CNNs)A transfer learning project using a pretrained modelSequential models - RNNs, LSTMs, and GRUs - for time-dependent dataA natural language processing pipeline, from raw text to predictionsA working introduction to Transformers and attention - the architecture behind modern AIA complete, end-to-end deep learning project, entirely your own Why This Book Is DifferentBackpropagation and gradient descent, genuinely explained - not waved at, not skipped, actually understood.An honest comparison with classical machine learning. You'll learn exactly when a neural network is the right tool - and when a random forest still wins.Regularization gets a full chapter, so your models generalize instead of memorizing.Computer vision and NLP both covered in depth - not just one, with the other as an afterthought.Worked solutions for every exercise, across all sixteen chapters.Who This Book Is For Written for readers who already have working comfort with Python, NumPy, pandas, and core machine learning vocabulary - training and test sets, overfitting, evaluation metrics (Book 1 of this series, Machine Learning for Absolute Beginners, builds exactly this foundation if you're starting fresh). If you can train and evaluate a scikit-learn model, you're ready for this book. You do not need prior exposure to neural networks, calculus beyond an intuitive sense of a derivative, or advanced linear algebra. Every mathematical idea is grounded in working code before it's ever discussed abstractly. What You'll Be Able to Do By the final chapter, you'll be able to design a neural network architecture for a given problem, train it without it overfitting, debug a model that refuses to learn, build genuine image classifiers and text models, and understand - concretely, not just by reputation - how today's AI systems actually work underneath. Deep learning is not magic, and it is not only for specialists. It's a buildable skill. This book builds it, one carefully explained layer at a time. Scroll up and start training your first neural network today. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…