Download or read book Natural Language Processing with Transformers Revised Edition written by Lewis Tunstall and published by "O'Reilly Media, Inc.". This book was released on 2022-05-26 with total page 409 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since their introduction in 2017, transformers have quickly become the dominant architecture for achieving state-of-the-art results on a variety of natural language processing tasks. If you're a data scientist or coder, this practical book -now revised in full color- shows you how to train and scale these large models using Hugging Face Transformers, a Python-based deep learning library. Transformers have been used to write realistic news stories, improve Google Search queries, and even create chatbots that tell corny jokes. In this guide, authors Lewis Tunstall, Leandro von Werra, and Thomas Wolf, among the creators of Hugging Face Transformers, use a hands-on approach to teach you how transformers work and how to integrate them in your applications. You'll quickly learn a variety of tasks they can help you solve. Build, debug, and optimize transformer models for core NLP tasks, such as text classification, named entity recognition, and question answering Learn how transformers can be used for cross-lingual transfer learning Apply transformers in real-world scenarios where labeled data is scarce Make transformer models efficient for deployment using techniques such as distillation, pruning, and quantization Train transformers from scratch and learn how to scale to multiple GPUs and distributed environments

Download or read book Transformers Cookbook written by Lauren Perry and published by . This book was released on 2021-03-15 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: Based on a collaborative American-Japanese franchise, the Transformers universe was known to everybody who grew up in the early 90s to the 2000s. It was extremely popular in the West during the time of the release of the movies on the franchise. They were released on the premise of living independent alien robots. And these robots were usually known as either Autobots or Decepticons, depending on the context that was being taken into consideration. The first few films that were released under the direction of Michael Bay were as follows; Transformers in 2007, Revenge of the Fallen in 2009, Dark of the Moon in 2011, Age of Extinction in 2014, and finally, The Last Knight was released in 2017. The spin-off based on the Autobot Bumblebee was released in 2018 under the direction of Travis Knight. Though critics weren't particularly keen on this sci-fi movie collection, it was still a brilliant hit among fans of the Transformers universe. Though the initial production of the universe began being circulated 4 decades ago, it hasn't lost its popularity in the current century.

Download or read book Mastering Transformers written by Savaş Yıldırım and published by Packt Publishing Ltd. This book was released on 2021-09-15 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a problem-solving approach to learning all about transformers and get up and running in no time by implementing methodologies that will build the future of NLP Key Features Explore quick prototyping with up-to-date Python libraries to create effective solutions to industrial problems Solve advanced NLP problems such as named-entity recognition, information extraction, language generation, and conversational AI Monitor your model's performance with the help of BertViz, exBERT, and TensorBoard Book DescriptionTransformer-based language models have dominated natural language processing (NLP) studies and have now become a new paradigm. With this book, you'll learn how to build various transformer-based NLP applications using the Python Transformers library. The book gives you an introduction to Transformers by showing you how to write your first hello-world program. You'll then learn how a tokenizer works and how to train your own tokenizer. As you advance, you'll explore the architecture of autoencoding models, such as BERT, and autoregressive models, such as GPT. You'll see how to train and fine-tune models for a variety of natural language understanding (NLU) and natural language generation (NLG) problems, including text classification, token classification, and text representation. This book also helps you to learn efficient models for challenging problems, such as long-context NLP tasks with limited computational capacity. You'll also work with multilingual and cross-lingual problems, optimize models by monitoring their performance, and discover how to deconstruct these models for interpretability and explainability. Finally, you'll be able to deploy your transformer models in a production environment. By the end of this NLP book, you'll have learned how to use Transformers to solve advanced NLP problems using advanced models.What you will learn Explore state-of-the-art NLP solutions with the Transformers library Train a language model in any language with any transformer architecture Fine-tune a pre-trained language model to perform several downstream tasks Select the right framework for the training, evaluation, and production of an end-to-end solution Get hands-on experience in using TensorBoard and Weights & Biases Visualize the internal representation of transformer models for interpretability Who this book is for This book is for deep learning researchers, hands-on NLP practitioners, as well as ML/NLP educators and students who want to start their journey with Transformers. Beginner-level machine learning knowledge and a good command of Python will help you get the best out of this book.

Download or read book Power Supply Cookbook written by Marty Brown and published by Elsevier. This book was released on 2001-06-13 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Power Supply Cookbook, Second Edition provides an easy-to-follow, step-by-step design framework for a wide variety of power supplies. With this book, anyone with a basic knowledge of electronics can create a very complicated power supply design in less than one day. With the common industry design approaches presented in each section, this unique book allows the reader to design linear, switching, and quasi-resonant switching power supplies in an organized fashion. Formerly complicated design topics such as magnetics, feedback loop compensation design, and EMI/RFI control are all described in simple language and design steps. This book also details easy-to-modify design examples that provide the reader with a design template useful for creating a variety of power supplies. This newly revised edition is a practical, "start-to-finish" design reference. It is organized to allow both seasoned and inexperienced engineers to quickly find and apply the information they need. Features of the new edition include updated information on the design of the output stages, selecting the controller IC, and other functions associated with power supplies, such as: switching power supply control, synchronization of the power supply to an external source, input low voltage inhibitors, loss of power signals, output voltage shut-down, major current loops, and paralleling filter capacitors. It also offers coverage of waveshaping techniques, major loss reduction techniques, snubbers, and quasi-resonant converters. Guides engineers through a step-by-step design framework for a wide variety of power supplies, many of which can be designed in less than one day Provides easy-to-understand information about often complicated topics, making power supply design a much more accessible and enjoyable process

Download or read book Transformers for Natural Language Processing written by Denis Rothman and published by Packt Publishing Ltd. This book was released on 2021-01-29 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Publisher's Note: A new edition of this book is out now that includes working with GPT-3 and comparing the results with other models. It includes even more use cases, such as casual language analysis and computer vision tasks, as well as an introduction to OpenAI's Codex. Key FeaturesBuild and implement state-of-the-art language models, such as the original Transformer, BERT, T5, and GPT-2, using concepts that outperform classical deep learning modelsGo through hands-on applications in Python using Google Colaboratory Notebooks with nothing to install on a local machineTest transformer models on advanced use casesBook Description The transformer architecture has proved to be revolutionary in outperforming the classical RNN and CNN models in use today. With an apply-as-you-learn approach, Transformers for Natural Language Processing investigates in vast detail the deep learning for machine translations, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains with transformers. The book takes you through NLP with Python and examines various eminent models and datasets within the transformer architecture created by pioneers such as Google, Facebook, Microsoft, OpenAI, and Hugging Face. The book trains you in three stages. The first stage introduces you to transformer architectures, starting with the original transformer, before moving on to RoBERTa, BERT, and DistilBERT models. You will discover training methods for smaller transformers that can outperform GPT-3 in some cases. In the second stage, you will apply transformers for Natural Language Understanding (NLU) and Natural Language Generation (NLG). Finally, the third stage will help you grasp advanced language understanding techniques such as optimizing social network datasets and fake news identification. By the end of this NLP book, you will understand transformers from a cognitive science perspective and be proficient in applying pretrained transformer models by tech giants to various datasets. What you will learnUse the latest pretrained transformer modelsGrasp the workings of the original Transformer, GPT-2, BERT, T5, and other transformer modelsCreate language understanding Python programs using concepts that outperform classical deep learning modelsUse a variety of NLP platforms, including Hugging Face, Trax, and AllenNLPApply Python, TensorFlow, and Keras programs to sentiment analysis, text summarization, speech recognition, machine translations, and moreMeasure the productivity of key transformers to define their scope, potential, and limits in productionWho this book is for Since the book does not teach basic programming, you must be familiar with neural networks, Python, PyTorch, and TensorFlow in order to learn their implementation with Transformers. Readers who can benefit the most from this book include experienced deep learning & NLP practitioners and data analysts & data scientists who want to process the increasing amounts of language-driven data.

Download or read book Transformers Rescue Bots Meet Quickshadow written by Brandon T. Snider and published by LB Kids. This book was released on 2017-04-11 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exciting leveled reader featuring Transformers Rescue Bots! A new Rescue Bot is in town! Meet Quickshadow. She is a secret agent. Optimus Prime wants her to learn about teamwork from the Rescue Bots. Can she do it? Passport to Reading Level 1 © 2017 Hasbro. All Rights Reserved.

Download or read book How to Feed a Family written by Laura Keogh and published by Appetite by Random House. This book was released on 2013-09-03 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: **Breakfast**Brunch**The Lunch Box**Snack Attack**Dinners**Desserts** What could be more important to parents than a healthy, well-fed family? As two urban, working moms, Ceri Marsh and Laura Keogh learned quickly how challenging healthy meal-times can be. So they joined forces to create the Sweet Potato Chronicles, a website written for, and by, non-judgemental moms, packed full of nutritious recipes for families. In the How to Feed a Family cookbook, Laura and Ceri have selected their very favorite recipes, to create a collection of more than 100 for all ages to enjoy. These are recipes that are tailored specifically to families: they are simple, fast, easy-to-follow, and use ingredients that are readily-available at your local grocery store. Ceri and Laura unveil their tried, tested and true tricks for turning nutritious, sophisticated dishes into kid-friendly masterpieces, that will guarantee you success at meal-time, time and time again. Interspersed with the recipes are parenting tips and advice to encourage happy meal-times for the whole family: get ready to turn your picky eaters into enthusiastic kitchen helpers!

Download or read book PyTorch Cookbook written by Matthew Rosch and published by GitforGits. This book was released on 2023-10-04 with total page 238 pages. Available in PDF, EPUB and Kindle. Book excerpt: Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters. The book simplifies neural networks, training, optimization, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasizes distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them. This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning. Key Learnings Comprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning. Practical demonstrations of various neural networks, enhancing understanding through hands-on practice. Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields. In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions. Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects. Real-world application insights, bridging the gap between theoretical knowledge and practical execution. Focus on mobile and embedded development with PyTorch, leading to on-device AI. Emphasis on error handling and troubleshooting, preparing readers for real-world challenges. Advanced topics like real-time inferencing and model compression, providing future ready skill. Table of Content Introduction to PyTorch 2.0 Deep Learning Building Blocks Convolutional Neural Networks Recurrent Neural Networks Natural Language Processing Graph Neural Networks (GNNs) Working with Popular PyTorch Tools Distributed Training and Scalability Mobile and Embedded Development

Download or read book Electronics Cookbook written by Simon Monk and published by "O'Reilly Media, Inc.". This book was released on 2017-03-31 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you’re among the many hobbyists and designers who came to electronics through Arduino and Raspberry Pi, this cookbook will help you learn and apply the basics of electrical engineering without the need for an EE degree. Through a series of practical recipes, you’ll learn how to solve specific problems while diving into as much or as little theory as you’re comfortable with. Author Simon Monk (Raspberry Pi Cookbook) breaks down this complex subject into several topics, from using the right transistor to building and testing projects and prototypes. With this book, you can quickly search electronics topics and go straight to the recipe you need. It also serves as an ideal reference for experienced electronics makers. This cookbook includes: Theoretical concepts such as Ohm’s law and the relationship between power, voltage, and current The fundamental use of resistors, capacitors and inductors, diodes, transistors and integrated circuits, and switches and relays Recipes on power, sensors and motors, integrated circuits, and radio frequency for designing electronic circuits and devices Advice on using Arduino and Raspberry Pi in electronics projects How to build and use tools, including multimeters, oscilloscopes, simulations software, and unsoldered prototypes

Download or read book The Regularization Cookbook written by Vincent Vandenbussche and published by Packt Publishing Ltd. This book was released on 2023-07-31 with total page 424 pages. Available in PDF, EPUB and Kindle. Book excerpt: Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to diagnose the need for regularization in any machine learning model Regularize different ML models using a variety of techniques and methods Enhance the functionality of your models using state of the art computer vision and NLP techniques Book Description Regularization is an infallible way to produce accurate results with unseen data, however, applying regularization is challenging as it is available in multiple forms and applying the appropriate technique to every model is a must. The Regularization Cookbook provides you with the appropriate tools and methods to handle any case, with ready-to-use working codes as well as theoretical explanations. After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. In the last five chapters, you'll discover regularization for deep learning models. After reviewing general methods that apply to any type of neural network, you'll dive into more NLP-specific methods for RNNs and transformers, as well as using BERT or GPT-3. By the end, you'll explore regularization for computer vision, covering CNN specifics, along with the use of generative models such as stable diffusion and Dall-E. By the end of this book, you'll be armed with different regularization techniques to apply to your ML and DL models. What you will learn Diagnose overfitting and the need for regularization Regularize common linear models such as logistic regression Understand regularizing tree-based models such as XGBoos Uncover the secrets of structured data to regularize ML models Explore general techniques to regularize deep learning models Discover specific regularization techniques for NLP problems using transformers Understand the regularization in computer vision models and CNN architectures Apply cutting-edge computer vision regularization with generative models Who this book is for This book is for data scientists, machine learning engineers, and machine learning enthusiasts, looking to get hands-on knowledge to improve the performances of their models. Basic knowledge of Python is a prerequisite.

Download or read book PySpark Cookbook written by Denny Lee and published by Packt Publishing Ltd. This book was released on 2018-06-29 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Combine the power of Apache Spark and Python to build effective big data applications Key Features Perform effective data processing, machine learning, and analytics using PySpark Overcome challenges in developing and deploying Spark solutions using Python Explore recipes for efficiently combining Python and Apache Spark to process data Book Description Apache Spark is an open source framework for efficient cluster computing with a strong interface for data parallelism and fault tolerance. The PySpark Cookbook presents effective and time-saving recipes for leveraging the power of Python and putting it to use in the Spark ecosystem. You’ll start by learning the Apache Spark architecture and how to set up a Python environment for Spark. You’ll then get familiar with the modules available in PySpark and start using them effortlessly. In addition to this, you’ll discover how to abstract data with RDDs and DataFrames, and understand the streaming capabilities of PySpark. You’ll then move on to using ML and MLlib in order to solve any problems related to the machine learning capabilities of PySpark and use GraphFrames to solve graph-processing problems. Finally, you will explore how to deploy your applications to the cloud using the spark-submit command. By the end of this book, you will be able to use the Python API for Apache Spark to solve any problems associated with building data-intensive applications. What you will learn Configure a local instance of PySpark in a virtual environment Install and configure Jupyter in local and multi-node environments Create DataFrames from JSON and a dictionary using pyspark.sql Explore regression and clustering models available in the ML module Use DataFrames to transform data used for modeling Connect to PubNub and perform aggregations on streams Who this book is for The PySpark Cookbook is for you if you are a Python developer looking for hands-on recipes for using the Apache Spark 2.x ecosystem in the best possible way. A thorough understanding of Python (and some familiarity with Spark) will help you get the best out of the book.

Download or read book Transformers Prime the Orion Pax Saga written by Mike Johnson and published by IDW Publishing. This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The animated action of Transformers is back for season 2! Picking up right where we left our heroes after the events of "One Shall Rise," Unicron has been defeated... but Optimus Prime has lost his memory! But not to worry, Megatron has plans for his newest recruit... unless the Autobots can stop their nemesis and save their leader!

Download or read book Deep Learning for Time Series Cookbook written by Vitor Cerqueira and published by Packt Publishing Ltd. This book was released on 2024-03-29 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to deal with time series data and how to model it using deep learning and take your skills to the next level by mastering PyTorch using different Python recipes Key Features Learn the fundamentals of time series analysis and how to model time series data using deep learning Explore the world of deep learning with PyTorch and build advanced deep neural networks Gain expertise in tackling time series problems, from forecasting future trends to classifying patterns and anomaly detection Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem.What you will learn Grasp the core of time series analysis and unleash its power using Python Understand PyTorch and how to use it to build deep learning models Discover how to transform a time series for training transformers Understand how to deal with various time series characteristics Tackle forecasting problems, involving univariate or multivariate data Master time series classification with residual and convolutional neural networks Get up to speed with solving time series anomaly detection problems using autoencoders and generative adversarial networks (GANs) Who this book is for If you’re a machine learning enthusiast or someone who wants to learn more about building forecasting applications using deep learning, this book is for you. Basic knowledge of Python programming and machine learning is required to get the most out of this book.

Download or read book Practical Natural Language Processing written by Sowmya Vajjala and published by O'Reilly Media. This book was released on 2020-06-17 with total page 455 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many books and courses tackle natural language processing (NLP) problems with toy use cases and well-defined datasets. But if you want to build, iterate, and scale NLP systems in a business setting and tailor them for particular industry verticals, this is your guide. Software engineers and data scientists will learn how to navigate the maze of options available at each step of the journey. Through the course of the book, authors Sowmya Vajjala, Bodhisattwa Majumder, Anuj Gupta, and Harshit Surana will guide you through the process of building real-world NLP solutions embedded in larger product setups. You’ll learn how to adapt your solutions for different industry verticals such as healthcare, social media, and retail. With this book, you’ll: Understand the wide spectrum of problem statements, tasks, and solution approaches within NLP Implement and evaluate different NLP applications using machine learning and deep learning methods Fine-tune your NLP solution based on your business problem and industry vertical Evaluate various algorithms and approaches for NLP product tasks, datasets, and stages Produce software solutions following best practices around release, deployment, and DevOps for NLP systems Understand best practices, opportunities, and the roadmap for NLP from a business and product leader’s perspective

Download or read book Machine Learning with Python Cookbook written by Kyle Gallatin and published by "O'Reilly Media, Inc.". This book was released on 2023-07-27 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical guide provides more than 200 self-contained recipes to help you solve machine learning challenges you may encounter in your work. If you're comfortable with Python and its libraries, including pandas and scikit-learn, you'll be able to address specific problems, from loading data to training models and leveraging neural networks. Each recipe in this updated edition includes code that you can copy, paste, and run with a toy dataset to ensure that it works. From there, you can adapt these recipes according to your use case or application. Recipes include a discussion that explains the solution and provides meaningful context. Go beyond theory and concepts by learning the nuts and bolts you need to construct working machine learning applications. You'll find recipes for: Vectors, matrices, and arrays Working with data from CSV, JSON, SQL, databases, cloud storage, and other sources Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Supporting vector machines (SVM), naäve Bayes, clustering, and tree-based models Saving, loading, and serving trained models from multiple frameworks

Download or read book Machine Learning Using TensorFlow Cookbook written by Alexia Audevart and published by Packt Publishing Ltd. This book was released on 2021-02-08 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive recipes to give you valuable insights on Transformers, Reinforcement Learning, and more Key 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 Description The 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 for If 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.

Download or read book Scala Cookbook written by Alvin Alexander and published by "O'Reilly Media, Inc.". This book was released on 2021-08-10 with total page 802 pages. Available in PDF, EPUB and Kindle. Book excerpt: Save time and trouble building object-oriented, functional, and concurrent applications with Scala. The latest edition of this comprehensive cookbook is packed with more than 250 ready-to-use recipes and 1,000 code examples to help you solve the most common problems when working with Scala 3 and its popular libraries. Scala changes the way you think about programming--and that's a good thing. Whether you're working on web, big data, or distributed applications, this cookbook provides recipes based on real-world scenarios for both experienced Scala developers and programmers just learning to use this JVM language. Author Alvin Alexander includes practical solutions from his experience using Scala for component-based, highly scalable applications that support concurrency and distribution. Recipes cover: Strings, numbers, and control structures Classes, methods, objects, traits, packaging, and imports Functional programming techniques Scala's wealth of collections classes and methods Building and publishing Scala applications with sbt Actors and concurrency with Scala Future and Akka Typed Popular libraries, including Spark, Scala.js, Play Framework, and GraalVM Types, such as variance, givens, intersections, and unions Best practices, including pattern matching, modules, and functional error handling