Click D

Allie Navarro can't wait to show her best friends the app she built at CodeGirls summer camp. Click'd pairs users based on common interests and sends them on a fun (and occasionally rule-breaking) scavenger hunt to find each other. And it's a hit. By the second day of school, everyone is talking about Click'd. Watching her app go viral is amazing. Leaderboards are filling up! Everyone's making new friends. And with all the data Allie is collecting, she has an even better shot at beating her archenemy, Nathan, at the upcoming youth coding competition. But when Allie discovers a glitch that threatens to expose everyone's secrets, she has to figure out how to make things right, even if that means sharing the computer lab with Nathan. Can Allie fix her app, stop it from doing any more damage, and win back the friends it hurt-all before she steps on stage to present Click'd to the judges? New York Times best-selling author Tamara Ireland Stone combines friendship, coding, and lots of popcorn in her fun and empowering middle-grade debut.

After her Click'd catastrophe, Allie Navarro is determined to redeem herself. So when the class gets an assignment to create a mobile game from recycled code, Allie pairs up with Courtney, her best friend from CodeGirls camp, to create the perfect app: Swap'd. Kids buy, sell, and trade stuff at school all the time. Candy. Clothes. Video games. Slime. Why not make a fiercely competitive, totally anonymous, beat-the-clock game out of it? Once Swap'd is in full-swing, Allie is certain that it's the answer to all her problems. She's making quick cash to help Courtney buy that really expensive plane ticket to come visit her. It's giving her an excuse to have an actual conversation with her super-secret crush. And it looks like she might finally beat her archenemy-turned-friend, Nathan. She's thought of everything. Or? has she? The second book in the Click'd series by New York Times best-selling author Tamara Ireland Stone weaves together middle school friendship, first crushes, and serious coding skills in another fun, fast-paced, and empowering novel that will have readers cheering Allie on from the first page to the last.

Allie Navarro can't wait to show her best friends the app she built at CodeGirls summer camp. Click'd pairs users based on common interests and sends them on a fun (and occasionally rule-breaking) scavenger hunt to find each other. And it's a hit. By the second day of school, everyone is talking about Click'd. Watching her app go viral is amazing. Leaderboards are filling up! Everyone's making new friends. And with all the data Allie is collecting, she has an even better shot at beating her archenemy, Nathan, at the upcoming youth coding competition. But when Allie discovers a glitch that threatens to expose everyone's secrets, she has to figure out how to make things right, even if that means sharing the computer lab with Nathan. Can Allie fix her app, stop it from doing any more damage, and win back the friends it hurt-all before she steps on stage to present Click'd to the judges? New York Times best-selling author Tamara Ireland Stone combines friendship, coding, and lots of popcorn in her fun and empowering middle-grade debut.

An encyclopedia designed especially to meet the needs of elementary, junior high, and senior high school students.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

Eleanor and Park meets Saved! in this moving contemporary novel from New York Times bestselling author Tamara Ireland Stone. Lifelong best friends and next-door neighbors Hannah and Emory have never gone a single day without talking. But now its senior year and they haven't spoken in three months. Not since the fight, where they each said things they couldn't take back. They're aching to break the silence, but those thirty-six steps between their bedroom windows feel more like thirty-six miles. Then one fateful night, Emory's boyfriend, Luke, almost dies. And Hannah is the one who finds him and saves his life. As Luke tries to make sense of his near-death experience, he secretly turns to Hannah, who becomes his biggest confidante. In Luke, Hannah finds someone she can finally talk to about all the questions she's grappling with. Emory just wants everything to go back to normal -- the way it was before the accident. She has no idea why her relationship is spiraling out of control. But when the horrifying reason behind Hannah and Emory's argument ultimately comes to light, all three of them will be forced work together to protect the one with the biggest secret of all. In the follow-up to her New York Times bestseller, Every Last Word, Tamara Ireland Stone crafts a deeply moving, unforgettable story about love, betrayal, and the power of friendship.

An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.