This book begins with an introduction to AI, followed by machine learning, deep learning, NLP, and reinforcement learning. Readers will learn about machine learning classifiers such as logistic regression, k-NN, decision trees, random forests, and SVMs. Next, the book covers deep learning architectures such as CNNs, RNNs, LSTMs, and auto encoders. Keras-based code samples are included to supplement the theoretical discussion. In addition, this book contains appendices for Keras, TensorFlow 2, and Pandas. Features: Covers an introduction to programming concepts related to AI, machine learning, and deep learning Includes material on Keras, TensorFlow2 and Pandas
This resource is designed to bridge the gap between theoretical understanding and practical application, making it a useful tool for software developers, data scientists, AI researchers, and tech enthusiasts interested in harnessing the power of GPT-4 in Python environments. The book contains an assortment of Python 3.x code samples that were generated by ChatGPT and GPT-4. Chapter 1 provides an overview of ChatGPT and GPT-4, followed by a chapter which contains Python 3.x code samples for solving various programming tasks in Python. Chapter 3 contains code samples for data visualization, and Chapter 4 contains code samples for linear regression. The final chapter covers visualization with Gen AI (Generative AI) and DALL-E. Companion files with source code and figures are available for downloading. FEATURES Offers an all-encompassing view of ChatGPT and GPT-4, from basics to advanced topics, including functionalities, capabilities, and limitations Contains Python 3.x code samples demonstrating the application of GPT-4 in real-world scenarios Provides a forward-looking perspective on Generative AI and its integration with data visualization and DALL-E Includes companion files with source code, data sets, and figures
This book begins with an overview of the Generative AI landscape, distinguishing it from conversational AI and shedding light on the roles of key players like DeepMind and OpenAI. It then reviews the intricacies of ChatGPT, GPT-4, and Gemini, examining their capabilities, strengths, and competitors. Readers will also gain insights into the BERT family of LLMs, including ALBERT, DistilBERT, and XLNet, and how these models have revolutionized natural language processing. Further, the book covers prompt engineering techniques, essential for optimizing the outputs of AI models, and addresses the challenges of working with LLMs, including the phenomenon of hallucinations and the nuances of fine-tuning these advanced models. Designed for software developers, AI researchers, and technology enthusiasts with a foundational understanding of AI, this book offers both theoretical insights and practical code examples in Python. Companion files with code, figures, and datasets are available for downloading from the publisher.
This book introduces an assortment of powerful command line utilities that can be combined to create simple, yet powerful shell scripts for processing datasets. The code samples and scripts use the bash shell, and typically involve small datasets so you can focus on understanding the features of grep, sed, and awk. Companion files with code are available for downloading from the publisher. FEATURES: Provides the reader with powerful command line utilities that can be combined to create simple yet powerful shell scripts for processing datasets Contains a variety of code fragments and shell scripts for data scientists, data analysts, and those who want shell-based solutions to “clean” various types of datasets Companion files with code
This book is intended for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks using Pandas and NumPy. It contains a variety of code samples and features of NumPy and Pandas, and how to write regular expressions. Chapter 3 includes fundamental statistical concepts and Chapter 7 covers data visualization with Matplotlib and Seaborn. Companion files with code are available for downloading from the publisher. FEATURES: Provides the reader with numerous code samples for Pandas and NumPy programming concepts, and an introduction to statistical concepts and data visualization Includes an introductory chapter on Python Companion files with code
This book is intended primarily for developers who have little or no experience with Python or Pandas. It contains a fast-paced introduction to Python and Python-based solutions to various tasks. Chapter 1 provides a quick tour of basic Python 3, followed by a chapter that shows how to work with loops and conditional logic in Python. Chapter 3 discusses data structures in Python, followed by a chapter that features code samples for tasks with strings and arrays in Python. Chapter 5 contains concepts in object-oriented programming, along with code samples that illustrate how they are implemented in Python. Chapter 6 introduces recursion and some fundamental topics in combinatorics. Finally, the appendix provides an introduction to Pandas. Companion files with code and figures are available for downloading from the publisher. Features: Provides the reader with basic Python 3 and Pandas programming concepts Companion files with code and figures
This book contains a fast-paced introduction to data-related tasks in preparation for training models on datasets. It presents a step-by-step, Python-based code sample that uses the kNN algorithm to manage a model on a dataset. Chapter One begins with an introduction to datasets and issues that can arise, followed by Chapter Two on outliers and anomaly detection. The next chapter explores ways for handling missing data and invalid data, and Chapter Four demonstrates how to train models with classification algorithms. Chapter 5 introduces visualization toolkits, such as Sweetviz, Skimpy, Matplotlib, and Seaborn, along with some simple Python-based code samples that render charts and graphs. An appendix includes some basics on using awk. Companion files with code, datasets, and figures are available for downloading. FEATURES: Covers extensive topics related to cleaning datasets and working with models Includes Python-based code samples and a separate chapter on Matplotlib and Seaborn Features companion files with source code, datasets, and figures from the book
This book is a fast-paced introduction to using data structures with Java. Numerous code samples and listings are included to support myriad topics. The first chapter contains a quick introduction to Java, along with Java code samples to check for leap years, find divisors of a number, and work with arrays of strings. The second chapter introduces recursion and usescode samples to check if a positive number is prime, to find the prime divisors of a positive integer, to calculate the GCD (greatest common divisor) and LCM (lowest common multiple) of a pair of positive integers. The third chapter contains Java code samples involving strings and arrays, such as finding binary substrings of a number, checking if strings contain unique characters, counting bits in a range of numbers, and how to compute XOR without using the XOR function. Chapters 4 through 6 include Java code samples involving search algorithms, concepts in linked lists, and tasks involving linked lists. Finally, Chapter 7 discusses data structures called queues and stacks, along with additional Java code samples. FEATURES: Extensive topics, code samples, and scripts related to data structures Covers strings, arrays, queues, and stacks, linked lists, computing the XOR function, checking for unique characters, and more Includes companion files with code samples from the book (available for downloading from the publisher)
This book eases you into the foundational aspects of Python 3.x with an extensive range of code samples that illustrate its diverse features. Start with Python tools and installations, and progressively learn intricacies like strings, loops, conditional logic, and much more. The appendices on NumPy and Pandas provide insights into efficient numerical operations, making it a holistic resource for novice programmers. Companion files with code samples are available for downloading from the publisher. FEATURES: Starts with the basics and advancing to complex topics, helping you grasp the essence of Python step-by-step Incorporates a multitude of practical tasks, aiding in reinforcing concepts and honing skills Includes appendices on NumPy and Pandas which furnish a concise introduction to numerical operations in Python, rounding off your beginner's learning curve Companion files with code samples are available for downloading from the publisher
This book is designed for data scientists, machine learning practitioners, and anyone with a foundational understanding of Python 3.x. In the evolving field of data science, the ability to manipulate and understand datasets is crucial. The book offers content for mastering these skills using Python 3. The book provides a fast-paced introduction to a wealth of feature engineering concepts, equipping readers with the knowledge needed to transform raw data into meaningful information. Inside, you’ll find a detailed exploration of various types of data, methodologies for outlier detection using Scikit-Learn, strategies for robust data cleaning, and the intricacies of data wrangling. The book further explores feature selection, detailing methods for handling imbalanced datasets, and gives a practical overview of feature engineering, including scaling and extraction techniques necessary for different machine learning algorithms. It concludes with a treatment of dimensionality reduction, where you’ll navigate through complex concepts like PCA and various reduction techniques, with an emphasis on the powerful Scikit-Learn framework. FEATURES Includes numerous practical examples and partial code blocks that illuminate the path from theory to application Explores everything from data cleaning to the subtleties of feature selection and extraction, covering a wide spectrum of feature engineering topics Offers an appendix on working with the “awk” command-line utility Features companion files available for downloading with source code, datasets, and figures
This book provides relevant information about intermediate Python 3.x for a variety of topics, such as comprehensions, iterators, generators, regular expressions, OOP, queues and stacks, and recursion. Each chapter contains an assortment of code samples that illustrate the topics in the chapter material. It assumes the reader is already familiar with Python programming and wants to expand programming skills in order to use Python in a variety of subject areas and disciplines such as business, mathematics, science, and engineering. Knowledge of other programming languages (such as Java) can also be helpful because of the exposure to programming concepts and object-oriented programming. This book includes companion files with numerous code samples, figures, and applications from the text.
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to TensorFlow 1.x fundamentals for basic machine learning algorithms in TensorFlow. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover deep learning and TensorFlow basics. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by writing to info@merclearning.com. Features: Uses Python for code samples Covers TensorFlow APIs and Datasets Assumes the reader has very limited experience Companion files with all of the source code examples (download from the publisher)
Python 3 and Data Visualization offers readers a deep dive into the world of Python 3 programming and the art of data visualization. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, seamlessly leading into the world of data visualization using prominent libraries such as Matplotlib. Chapter 6 immerses the reader in Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. The appendix covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. The book also includes companion files with numerous Python code samples and figures. From foundational Python concepts to the intricacies of data visualization, this book serves as a comprehensive resource for both beginners and seasoned professionals. FEATURES: Covers numerous tools for mastering visualization including NumPy, Pandas, SQL, Matplotlib, and Seaborn Includes an introductory chapter on Python 3 basics Features companion files with numerous Python code samples and figures
This book covers the features of HTML5 Canvas, CSS3 graphics, and shows how you can extend the power of CSS3 with SVG. The material in this book is accessible to people who have limited knowledge of HTML and JavaScript. Companion DVD with source code and graphics. While the material is accessible to those with limited knowledge of HTML and JavaScript, but more advanced users will benefit from numerous graphics techniques. The book also includes illustrative code samples and illustrations that are useful for Web developers and SVG/Flash/Silverlight developers. You'll see examples that help you learn to do the following in HTML5 Canvas, CSS3, and SVG: render Bezier curves, apply colors and gradients, transform 2D shapes and JPG files, perform animation effects, create 2D/3D bar charts and line graphs, handle mouse events, render HTML5/CSS3/SVG pages in Android, and learn the mechanics of a Tic-tac-toe game. A companion DVD contains all the source code and color graphics from the book.
This book is intended primarily for people who want to learn both Python 3 and how to use ChatGPT with Python. Chapter One begins with an introduction to fundamental aspects of Python programming, including various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. Later, the book covers loops, conditional logic, and reserved words in Python. You will also see how to handle user input, manage exceptions, and work with command-line arguments. Next, the text transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including ChatGPT, GPT-4, and their competitors, are presented to give readers an understanding of the current AI landscape. The book also sheds light on the capabilities of ChatGPT, its strengths, weaknesses, and potential applications. In addition, you will learn how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin. Code samples and figures from the book are available for downloading. In essence, the book provides a modest bridge between the worlds of Python programming and AI, aiming to equip readers with the knowledge and skills to navigate both domains confidently. FEATURES Includes a chapter on how to generate a variety of Python 3 code samples via ChatGPT using the “Code Interpreter” plugin Covers basic concepts of Python 3 such as loops, conditional logic, reserved words, user input, manage exceptions, work with command-line arguments, and more Includes companion files for downloading with source code and figures
This book is designed to provide the reader with basic Python 3 programming concepts related to machine learning. The first four chapters provide a fast-paced introduction to Python 3, NumPy, and Pandas. The fifth chapter introduces the fundamental concepts of machine learning. The sixth chapter is devoted to machine learning classifiers, such as logistic regression, k-NN, decision trees, random forests, and SVMs. The final chapter includes material on NLP and RL. Keras-based code samples are included to supplement the theoretical discussion. The book also contains separate appendices for regular expressions, Keras, and TensorFlow 2. Features: Provides the reader with basic Python 3 programming concepts related to machine learning Includes separate appendices for regular expressions, Keras, and TensorFlow 2
As part of the best-selling Pocket Primer series, this book is designed to prepare programmers for machine learning and deep learning/TensorFlow topics. It begins with a quick introduction to Python, followed by chapters that discuss NumPy, Pandas, Matplotlib, and scikit-learn. The final two chapters contain an assortment of TensorFlow 1.x code samples, including detailed code samples for TensorFlow Dataset (which is used heavily in TensorFlow 2 as well). A TensorFlow Dataset refers to the classes in the tf.data.Dataset namespace that enables programmers to construct a pipeline of data by means of method chaining so-called lazy operators, e.g., map(), filter(), batch(), and so forth, based on data from one or more data sources. Companion files with source code are available for downloading from the publisher by writing info@merclearning.com. Features: A practical introduction to Python, NumPy, Pandas, Matplotlib, and introductory aspects of TensorFlow 1.x Contains relevant NumPy/Pandas code samples that are typical in machine learning topics, and also useful TensorFlow 1.x code samples for deep learning/TensorFlow topics Includes many examples of TensorFlow Dataset APIs with lazy operators, e.g., map(), filter(), batch(), take() and also method chaining such operators Assumes the reader has very limited experience Companion files with all of the source code examples (download from the publisher)
This book is intended primarily for those who plan to become data scientists as well as anyone who needs to perform data cleaning tasks. It contains a variety of features of NumPy and Pandas and how to create databases and tables in MySQL. Chapter 7 covers many data wrangling tasks using Python scripts and awk-based shell scripts. Companion files with code are available for downloading from the publisher. Features: Provides the reader with basic Python 3, Java, and Pandas programming concepts, and an introduction to awk Includes a chapter on RDBMs and SQL Companion files with code
The purpose of this book is to usher readers into the world of data, ensuring a comprehensive understanding of its nuances, intricacies, and complexities. With Python 3 as the primary medium, the book underscores the pivotal role of data in modernindustries, and how its adept management can lead to insightful decision-making. The book provides a quick introduction to foundational data-related tasks, priming the readers for more advanced concepts of model training introduced later on. Through detailed, step-by-step Python code examples, the reader will master training models, beginning with the kNN algorithm, and then smoothly transitioning to other classifiers, by tweaking mere lines of code. Tools like Sweetviz, Skimpy, Matplotlib, and Seaborn are introduced, offering readers a hands-on experience in rendering charts and graphs. Companion files with source code and data sets are available by writing to the publisher. FEATURES: Introduces tools like Sweetviz, Skimpy, Matplotlib, and Seaborn offering readers a hands-on experience in rendering charts and graphs Companion files with numerous Python code samples
As part of the best-selling Pocket Primer series, this book is designed to present the fundamentals of data structures using Python. Data structures provide a means to manage huge amounts of information such as large databases and the ability to use search and sort algorithms effectively. It is intended to be a fast-paced introduction to the core concepts of Python and data structures, illustrated with numerous code samples. Companion files with source code are available for downloading. FEATURES: Begins with an introduction to Python, and covers recursion, strings, search and sort, linked lists, stacks, and more Features numerous code samples throughout Includes companion files with source code available for downloading.
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of data analytics using Python 3. It is intended to be a fast-paced introduction to some basic features of data analytics and also covers statistics, data visualization, and data cleaning. The book includes numerous code samples using NumPy, Pandas, Matplotlib, Seaborn, and features an appendix on regular expressions. Companion files with source code and color figures are available online by emailing the publisher with proof of purchase at info@merclearning.com. FEATURES: Includes a concise introduction to Python 3 Provides a thorough introduction to data and data cleaning Covers NumPy and Pandas Introduces statistical concepts and data visualization (Matplotlib/Seaborn) Features an appendix on regular expressions Includes companion files with source code and figures
As part of the best-selling Pocket Primer series, this book is designed to introduce beginners to basic machine learning algorithms using TensorFlow 2. It is intended to be a fast-paced introduction to various “core” features of TensorFlow, with code samples that cover machine learning and TensorFlow basics. A comprehensive appendix contains some Keras-based code samples and the underpinnings of MLPs, CNNs, RNNs, and LSTMs. The material in the chapters illustrates how to solve a variety of tasks after which you can do further reading to deepen your knowledge. Companion files with all of the code samples are available for downloading from the publisher by emailing proof of purchase to info@merclearning.com. Features: Uses Python for code samples Covers TensorFlow 2 APIs and Datasets Includes a comprehensive appendix that covers Keras and advanced topics such as NLPs, MLPs, RNNs, LSTMs Features the companion files with all of the source code examples and figures (download from the publisher)
Covers the features of HTML5, CSS3 graphics, jQuery, and jQuery Mobile, and also shows how you can extend the power of CSS3 with SVG. Designed for readers with some knowledge of CSS/HTML/JavaScript, but more advanced users will benefit from numerous graphics techniques that are illustrated in many code samples. DVD with code and graphics included. You’ll see examples that help you learn to: create mobile Web applications using jQuery and jQuery Mobile; render HTML5/CSS3/SVG Web pages in Android and iOS; and create 2D/3D graphics & animation effects with CSS3. A companion DVD with source code and graphics is included. FEATURES • Learn how to create Web Pages with jQuery and jQuery Mobile • Create mobile apps in Android and iOS with HTML5/CSS3/SVG • Create Web Pages with jQuery with CSS3 • Learn about upcoming CSS3 features such as CSS3 Shaders and Regions • Create 2D/3D graphics and animation effects with CSS3 • Render 2D shapes, charts, and graphs with gradients in HTML5 Canvas • Includes companion DVD with source code and 4-color graphics
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic deep learning concepts and incorporate that knowledge into Angular 10 applications. It is intended to be a fast-paced introduction to some basic features of deep learning and an overview of several popular deep learning classifiers. The book includes code samples and numerous figures and covers topics such as Angular 10 functionality, basic deep learning concepts, classification algorithms, TensorFlow, and Keras. Companion files with code and color figures are included. FEATURES: Introduces basic deep learning concepts and Angular 10 applications Covers MLPs (MultiLayer Perceptrons) and CNNs (Convolutional Neural Networks), RNNs (Recurrent Neural Networks), LSTMs (Long Short-Term Memory), GRUs (Gated Recurrent Units), autoencoders, and GANs (Generative Adversarial Networks) Introduces TensorFlow 2 and Keras Includes companion files with source code and 4-color figures. The companion files are also available online by emailing the publisher with proof of purchase at info@merclearning.com.
This book is for developers who are looking for an overview of basic concepts in Natural Language Processing using R. It casts a wide net of techniques to help developers who have a range of technical backgrounds. Numerous code samples and listings are included to support myriad topics. The final chapter presents the Transformer Architecture, BERT-based models, and the GPT family of models, all of which were developed during the past three years. Companion files with source code and figures are included and available for downloading by emailing the publisher at info@merclearning.com with proof of purchase. FEATURES: Covers extensive topics related to natural language processing using R Features companion files with source code and figures from the book
As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapters on data visualization and working with Sklearn and SciPy. Companion files with source code are available. FEATURES: Introduces Python, NumPy, Sklearn, SciPy, and awk Covers data cleaning tasks and data visualization Features numerous code samples throughout Includes companion files with source code
This book provides a bridge between the worlds of Python 3 programming and Generative AI, aiming to equip readers with the skills to navigate both domains with confidence. It begins with an introduction to fundamental aspects of Python programming, which include various data types, number formatting, Unicode and UTF-8 handling, and text manipulation techniques. In addition, you will learn about loops, functions, data structures, NumPy, Pandas, conditional logic, and reserved words in Python. Further chapters show how to handle user input, manage exceptions, and work with command-line arguments. The text then transitions to the realm of Generative AI, discussing its distinction from Conversational AI. Popular platforms and models, including Bard (now called “Gemini”) and its competitors, are presented to give readers an understanding of the current AI landscape. The book discusses the capabilities of Bard, its strengths, weaknesses, and potential applications. Finally, you will learn how to generate a variety of Python 3 code samples via Bard.
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to the basic concepts of managing data using a variety of computer languages and applications. It is intended to be a fast-paced introduction to some basic features of data management and covers statistical concepts, data-related techniques, features of Pandas, RDBMS, SQL, NLP topics, Matplotlib, and data visualization. Companion files with source code and color figures are available. FEATURES: Covers Pandas, RDBMS, NLP, data cleaning, SQL, and data visualization Introduces probability and statistical concepts Features numerous code samples throughout Includes companion files with source code and figures
As part of the best-selling Pocket Primer series, this book is designed to introduce the reader to basic machine learning concepts and incorporate that knowledge into Angular applications. The book is intended to be a fast-paced introduction to some basic features of machine learning and an overview of several popular machine learning classifiers. It includes code samples and numerous figures and covers topics such as Angular functionality, basic machine learning concepts, classification algorithms, TensorFlow and Keras. The files with code and color figures are on the companion disc with the book or available from the publisher. Features: Introduces the basic machine learning concepts and Angular applications Includes source code and full color figures
This book is designed to bridge the gap between theoretical knowledge and practical application in the fields of Python programming, machine learning, and the innovative use of ChatGPT-4 in data science. The book is structured to facilitate a deep understanding of several core topics. It begins with a detailed introduction to Pandas, a cornerstone Python library for data manipulation and analysis. Next, it explores a variety of machine learning classifiers from kNN to SVMs. In later chapters, it discusses the capabilities of GPT-4, and how its application enhances traditional linear regression analysis. Finally, the book covers the innovative use of ChatGPT in data visualization. This segment focuses on how AI can transform data into compelling visual stories, making complex results accessible and understandable. It includes material on AI apps, GANs, and DALL-E. Companion files are available for downloading with code and figures from the text. FEATURES: Includes practical tutorials designed to provide hands-on experience, reinforcing learning through practice Provides coverage of the latest Python tools using state-of-the-art libraries essential for modern data scientists Companion files with source code, datasets, and figures are available for downloading
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques. FEATURES Explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories Contains detailed tutorials that guide you through the creation of complex visuals Tackles actual data scenarios and builds your expertise as you apply learned concepts to real datasets Features data manipulation and cleaning with Pandas to prepare flawless datasets ready for visualization Includes companion files with source code, data sets, and figures
This book is designed for those new to Java and interested in understanding how ChatGPT/GPT-4 can enhance programming. It offers a unique approach to learning Java, combining traditional hand-written code with cutting-edge ChatGPT-generated examples. The book covers the basics of Java programming and development environments, including understanding recursion, strings, arrays, fundamental data structures, algorithm analysis, queues and stacks, and follows with the role of ChatGPT in generating, explaining, and debugging code. Companion files with source code and figures available for downloading. It’s an essential resource for those starting Java programming and for anyone curious about the applications of ChatGPT in coding. FEATURES Combines hand-crafted Java code with ChatGPT-generated examples for a multifaceted learning experience Offers practical Java coding skills, with examples in recursion, data structures, and algorithm analysis Covers the capabilities of ChatGPT for code generation, debugging, and explanation, providing a modern perspective on programming Includes companion files for downloading with source code and figures
As part of the best selling Pocket Primer series, this book is an effort to give programmers sufficient knowledge of data cleaning to be able to work on their own projects. It is designed as a practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks. The book is packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together. Companion files with source code are available for downloading from the publisher. Features: - A practical introduction to using flexible, powerful (and free) Unix / Linux shell commands to perform common data cleaning tasks - Includes the concept of piping data between commands, regular expression substitution, and the sed and awk commands - Packed with realistic examples and numerous commands that illustrate both the syntax and how the commands work together - Assumes the reader has no prior experience, but the topic is covered comprehensively enough to teach a pro some new tricks - Includes companion files with all of the source code examples (download from the publisher).
This book offers a comprehensive guide to leveraging Python-based data visualization techniques with the innovative capabilities of Google Gemini. Tailored for individuals proficient in Python seeking to enhancetheir visualization skills, it explores essential libraries like Pandas, Matplotlib, and Seaborn, along with insights into the innovative Gemini platform. With a focus on practicality and efficiency, it delivers a rapid yet thorough exploration of data visualization methodologies, supported by Gemini-generated code samples. Companion files with source code and figures are available for downloading. FEATURES: Covers Python-based data visualization libraries and techniques Includes practical examples and Gemini-generated code samples for efficient learning Integrates Google Gemini for advanced data visualization capabilities Sets up a conducive development environment for a seamless coding experience Includes companion files for downloading with source code and figures
This book is designed to show readers the concepts of Python 3 programming and the art of data visualization. It also explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories. Chapter 1 introduces the essentials of Python, covering a vast array of topics from basic data types, loops, and functions to more advanced constructs like dictionaries, sets, and matrices. In Chapter 2, the focus shifts to NumPy and its powerful array operations, leading into data visualization using prominent libraries such as Matplotlib. Chapter 6 includes Seaborn's rich visualization tools, offering insights into datasets like Iris and Titanic. Further, the book covers other visualization tools and techniques, including SVG graphics, D3 for dynamic visualizations, and more. Chapter 7 covers information about the main features of ChatGPT and GPT-4, as well as some of their competitors. Chapter 8 contains examples of using ChatGPT in order to perform data visualization, such as charts and graphs that are based on datasets (e.g., the Titanic dataset). Companion files with code, datasets, and figures are available for downloading. From foundational Python concepts to the intricacies of data visualization, this book is ideal for Python practitioners, data scientists, and anyone in the field of data analytics looking to enhance their storytelling with data through visuals. It's also perfect for educators seeking material for teaching advanced data visualization techniques. FEATURES Explores cutting-edge techniques using ChatGPT/GPT-4 in harmony with Python for generating visuals that tell more compelling data stories Contains detailed tutorials that guide you through the creation of complex visuals Tackles actual data scenarios and builds your expertise as you apply learned concepts to real datasets Features data manipulation and cleaning with Pandas to prepare flawless datasets ready for visualization Includes companion files with source code, data sets, and figures
As part of the new Pocket Primer series, this book provides an overview of the major aspects and the source code to use Python 2. It covers the latest Python developments, built-in functions and custom classes, data visualization, graphics, databases, and more. It includes a companion disc with appendices, source code, and figures. This Pocket Primer is primarily for self-directed learners who want to learn Python 2 and it serves as a starting point for deeper exploration of Python programming. Features: +Includes a companion disc with appendices, source code, and figures +Contains material devoted to Raspberry Pi, Roomba, JSON, and Jython +Includes latest Python 2 developments, built-in functions and custom classes, data visualization, graphics, databases, and more +Provides a solid introduction to Python 2 via complete code samples On the CD-ROM: +Appendices (HTML5 and JavaScript Toolkits, Jython, SPA) +Source code samples +All images from the text (including 4-color) +Solutions to Odd-Numbered Exercises
As part of the best-selling Pocket Primer series, this book is primarily for data scientists and machine learning engineers who want to expand their current knowledge of SQL using MySQL as the primary RDBMS. It includes Python-based code samples to access data from a MySQL table in a Pandas data frame and Java-based code samples for accessing data in a MySQL database, along with XML documents and JSON documents. The book also introduces NoSQL, presents an overview of MongoDB, and SQLite--an open-source RDBMS available on mobile devices. The final chapter of the book covers a diverse set of miscellaneous topics, such as normalization, schemas, database optimization, and performance. Numerous code samples and listings are included to support myriad topics. Companion files with source code and figures are available from the publisher. FEATURES: Covers extensive topics related to SQL, using MySQL as the primary RDBMS Introduces NoSQL, presents an overview of MongoDB, and SQLite--an open-source RDBMS available on mobile devices Features companion files with source code and figures from the book
As part of the Pocket Primer series, this book provides an overview of the major aspects and the source code to use D3. This Pocket Primer is primarily for self-directed learners who want to learn D3 and serves as a starting point for deeper exploration of its programming. Features: • Includes a companion disc with appendices, source code, and figures • Contains material devoted to D3 on mobile devices, using D3 with Ajax, HTML5 Web Sockets, NodeJS, and covers D3 application programming interfaces and other toolkits • Provides a solid introduction to D3 via complete code samples eBook Customers: Companion files are available for downloading with order number/proof of purchase by writing to the publisher at info@merclearning.com.
This book provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Spanning across ten chapters, it begins with foundational concepts such as the attention mechanism, then tokenization techniques, explores the nuances of Transformer and BERT architectures, and culminates in advanced topics related to the latest in the GPT series, including ChatGPT. Key chapters provide insights into the evolution and significance of attention in deep learning, the intricacies of the Transformer architecture, a two-part exploration of the BERT family, and hands-on guidance on working with GPT-3. The concluding chapters present an overview of ChatGPT, GPT-4, and visualization using generative AI. In addition to the primary topics, the book also covers influential AI organizations such as DeepMind, OpenAI, Cohere, Hugging Face, and more. Readers will gain a comprehensive understanding of the current landscape of NLP models, their underlying architectures, and practical applications. Features companion files with numerous code samples and figures from the book. FEATURES: Provides a comprehensive group of topics covering the details of the Transformer architecture, BERT models, and the GPT series, including GPT-3 and GPT-4. Features companion files with numerous code samples and figures from the book.
As part of the new Pocket Primer series, this book provides an overview of the major aspects, the source code, and tutorial videos to use jQuery. DVD with code, videos, and graphics included. Features: • Integrated coverage of CSS3, jQuery and other important JS toolkits • Covers jQuery Mobile and HTML5 hybrid mobile apps • Covers BackboneJS and Twitter Bootstrap • Includes companion DVD with source code, tutorial videos, and 4-color graphics
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