Search results for "generative ai with python and tensorflow"
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R 525.00
R 648.00
Interpretable Machine Learning with Python
Understand the key aspects and challenges of machine learning interpretability, learn how to overcome them with interpretation methods, and leverage them to build fairer, safer, and more reliable modelsKey FeaturesLearn how to extract easy-to-understand insights from any machine learning modelBecome well-versed with interpretability techniques to build fairer, safer, and more reliable modelsMitigate risks in AI systems before they have broader implications by learning how to debug black-box modelsBook DescriptionDo you want to understand your models and mitigate risks associated with poor predictions using machine learning (ML) interpretation? Interpretable Machine Learning with Python can help you work effectively with ML models. The first section of the book is a beginner's guide to interpretability, covering its relevance in business and exploring its key aspects and challenges. You'll focus on how white-box models work, compare them to black-box and glass-box models, and examine their trade-off. The second section will get you up to speed with a vast array of interpretation methods, also known as Explainable AI (XAI) methods, and how to apply them to different use cases, be it for classification or regression, for tabular, time-series, image or text. In addition to the step-by-step code, the book also helps the reader to interpret model outcomes using examples. In the third section, you’ll get hands-on with tuning models and training data for interpretability by reducing complexity, mitigating bias, placing guardrails, and enhancing reliability. The methods you’ll explore here range from state-of-the-art feature selection and dataset debiasing methods to monotonic constraints and adversarial retraining. By the end of this book, you'll be able to understand ML models better and enhance them through interpretability tuning.What you will learnRecognize the importance of interpretability in businessStudy models that are intrinsically interpretable such as linear models, decision trees, and Naïve BayesBecome well-versed in interpreting models with model-agnostic methodsVisualize how an image classifier works and what it learnsUnderstand how to mitigate the influence of bias in datasetsDiscover how to make models more reliable with adversarial robustnessUse monotonic constraints to make fairer and safer modelsWho this book is forThis book is for data scientists, machine learning developers, and data stewards who have an increasingly critical responsibility to explain how the AI systems they develop work, their impact on decision making, and how they identify and manage bias. Working knowledge of machine learning and the Python programming language is expected.
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Fear Not, Creative
Thrive in an increasingly synthetic and saturated marketplace with this creative rebellion manual for musicians, artists, designers, and makers of all types.Music producer Steven Potaczek cuts through the hype and hysteria surrounding generative AI, providing tools that safeguard and amplify the unique voices of artists. With clarity, practical guidance, and proven strategies, it shows how creatives of all kinds can turn disruption into opportunity.Readers will learn how to: - Decode the AI threat with clarity and confidence- Transform anxiety into action and reclaim creative power- Develop a bold artistic voice that cuts through the noise and AI slop- Build creative confidence by vulnerably leaning into their humanity- Protect income and position themselves as irreplaceably human in a content-saturated economyThrough a compelling mix of historical context, advice, and cultural critique, this book unpacks what generative AI really is, demystifies buzzwords, and reveals where machines fall short. By reexamining and redefining value in the contemporary creative economy, Potaczek offers clear guidance on how to stand out, build loyalty, and create work that resonates far beyond the algorithmic churn of AI. Amid the anxiety and uncertainty of an increasingly automated world, this book delivers the definitive playbook for making art – and making a living – in the age of AI.
R 796.00
Interactive Data Visualization with Python
Create your own clear and impactful interactive data visualizations with the powerful data visualization libraries of PythonKey FeaturesStudy and use Python interactive libraries, such as Bokeh and PlotlyExplore different visualization principles and understand when to use which oneCreate interactive data visualizations with real-world dataBook DescriptionWith so much data being continuously generated, developers, who can present data as impactful and interesting visualizations, are always in demand. Interactive Data Visualization with Python sharpens your data exploration skills, tells you everything there is to know about interactive data visualization in Python.You'll begin by learning how to draw various plots with Matplotlib and Seaborn, the non-interactive data visualization libraries. You'll study different types of visualizations, compare them, and find out how to select a particular type of visualization to suit your requirements. After you get a hang of the various non-interactive visualization libraries, you'll learn the principles of intuitive and persuasive data visualization, and use Bokeh and Plotly to transform your visuals into strong stories. You'll also gain insight into how interactive data and model visualization can optimize the performance of a regression model.By the end of the course, you'll have a new skill set that'll make you the go-to person for transforming data visualizations into engaging and interesting stories.What you will learnExplore and apply different interactive data visualization techniquesManipulate plotting parameters and styles to create appealing plotsCustomize data visualization for different audiencesDesign data visualizations using interactive librariesUse Matplotlib, Seaborn, Altair and Bokeh for drawing appealing plotsCustomize data visualization for different scenariosWho this book is forThis book intends to provide a solid training ground for Python developers, data analysts and data scientists to enable them to present critical data insights in a way that best captures the user's attention and imagination. It serves as a simple step-by-step guide that demonstrates the different types and components of visualization, the principles, and techniques of effective interactivity, as well as common pitfalls to avoid when creating interactive data visualizations. Students should have an intermediate level of competency in writing Python code, as well as some familiarity with using libraries such as pandas.
R 1,661.00
Crafting Test-Driven Software with Python
Get to grips with essential concepts and step-by-step explanations to apply TDD practices to your Python projects while keeping your test suite under controlKey FeaturesBuild robust Python applications using TDD and BDD methodologiesTest Python web applications using WebTest and web frameworksLeverage PyTest to implement stringent testing mechanisms to ensure fault-tolerant applicationsBook DescriptionTest-driven development (TDD) is a set of best practices that helps developers to build more scalable software and is used to increase the robustness of software by using automatic tests. This book shows you how to apply TDD practices effectively in Python projects. You’ll begin by learning about built-in unit tests and Mocks before covering rich frameworks like PyTest and web-based libraries such as WebTest and Robot Framework, discovering how Python allows you to embrace all modern testing practices with ease. Moving on, you’ll find out how to design tests and balance them with new feature development and learn how to create a complete test suite with PyTest. The book helps you adopt a hands-on approach to implementing TDD and associated methodologies that will have you up and running and make you more productive in no time. With the help of step-by-step explanations of essential concepts and practical examples, you’ll explore automatic tests and TDD best practices and get to grips with the methodologies and tools available in Python for creating effective and robust applications. By the end of this Python book, you will be able to write reliable test suites in Python to ensure the long-term resilience of your application using the range of libraries offered by Python for testing and development.What you will learnFind out how tests can make your life easier as a developer and discover related best practicesExplore PyTest, the most widespread testing framework for PythonGet to grips with the most common PyTest plugins, including coverage, flaky, xdist, and pickedWrite functional tests for WSGI web applications with WebTestRun end-to-end tests for web applications using Robot FrameworkUnderstand what test-driven development means and why it is importantDiscover how to use the range of tools available in PythonBuild reliable and robust applicationsWho this book is forThis book is for Python developers looking to get started with test-driven development and developers who want to learn about the testing tools available in Python. Developers who want to create web applications with Python and plan to implement TDD methodology with PyTest will find this book useful. Basic knowledge of Python programming is required.
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R 690.41
Data-Driven SEO with Python
Solve SEO problems using data science. This hands-on book is packed with Python code and data science techniques to help you generate data-driven recommendations and automate the SEO workload. This book is a practical, modern introduction to data science in the SEO context using Python. With social media, mobile, changing search engine algorithms, and ever-increasing expectations of users for super web experiences, too much data is generated for an SEO professional to make sense of in spreadsheets. For any modern-day SEO professional to succeed, it is relevant to find an alternate solution, and data science equips SEOs to grasp the issue at hand and solve it. From machine learning to Natural Language Processing (NLP) techniques, Data-Driven SEO with Python provides tried and tested techniques with full explanations for solving both everyday and complex SEO problems.This book is ideal for SEO professionals who want to take their industry skills to the next level and enhance their business value, whether they are a new starter or highly experienced in SEO, Python programming, or both. What You'll LearnSee how data science works in the SEO contextThink about SEO challenges in a data driven wayApply the range of data science techniques to solve SEO issuesUnderstand site migration and relaunches areWho This Book Is ForSEO practitioners, either at the department head level or all the way to the new career starter looking to improve their skills. Readers should have basic knowledge of Python to perform tasks like querying an API with some data exploration and visualization.
R 912.00
R 331.08
Foundation Dynamic Web Pages with Python
Discover the concepts of creating dynamic web pages (HTML) with Python. This book reviews several methods available to serve up dynamic HTML including CGI, SSI, Django, and Flask. You will start by covering HTML pages and CSS in general and then move on to creating pages via CGI. It is easy to use and can serve as a foundation for the more advanced services available for launching dynamic web pages. Next you'll explore the SSI (Server Side Interface) method. This is a slightly more advanced interface included in mots web servers that adds functionality to modify static HTML pages to add such things as the current date or time, include additional HTML, and other features to a static web page before it is delivered to the user. The book also covers some of the key the Django module features, which must be added to the web server. These features include creating dynamic web pages and calling a database to provide additional information to the web page. Lastly you will explore the Flask module. While it has limited functionality on its own, it provides a very flexible environment to create a self designed system for delivery of dynamic web pages.By the time you finish this book, you will be able to choose the appropriate methodology for delivering dynamic information using fast HTML creation services.What You'll LearnUse HTML pages and CSS together to control the style of your web site.Install and configure SSI, Django, and Flask for Apache.Create dynamic web pages using CGI and creating a library of partial HTML pages to use in this task.Build dynamic web pages using SSI and auxiliary Python programs to enhance the SSI functionality.Develop dynamic web pages using Django.Who This Book Is ForSoftware Developers with basic Python programming skills interested in learning Web Development
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R 753.00
Productive and Efficient Data Science with Python
This book focuses on the Python-based tools and techniques to help you become highly productive at all aspects of typical data science stacks such as statistical analysis, visualization, model selection, and feature engineering.You’ll review the inefficiencies and bottlenecks lurking in the daily business process and solve them with practical solutions. Automation of repetitive data science tasks is a key mindset that is promoted throughout the book. You’ll learn how to extend the existing coding practice to handle larger datasets with high efficiency with the help of advanced libraries and packages that already exist in the Python ecosystem. The book focuses on topics such as how to measure the memory footprint and execution speed of machine learning models, quality test a data science pipelines, and modularizing a data science pipeline for app development. You’ll review Python libraries which come in very handy for automating and speeding up the day-to-day tasks. In the end, you’ll understand and perform data science and machine learning tasks beyond the traditional methods and utilize the full spectrum of the Python data science ecosystem to increase productivity. What You’ll Learn Write fast and efficient code for data science and machine learningBuild robust and expressive data science pipelinesMeasure memory and CPU profile for machine learning methodsUtilize the full potential of GPU for data science tasks Handle large and complex data sets efficiently Who This Book Is For Data scientists, data analysts, machine learning engineers, Artificial intelligence practitioners, statisticians who want to take full advantage of Python ecosystem.
R 1,482.00
Learn Raspberry Pi Programming with Python
Learn how to program your nifty new $35 computer to make a web spider, a weather station, a media server, and more. This book explores how to make a variety of fun and even useful projects, from a web bot to search and download files to a toy to drive your pets insane.Even if you're completely new to programming in general, you'll see how easy it is to create a home security system, an underwater photography system, an RC plane with a camera, and even a near-space weather balloon with a camera. You'll learn how to use Pi with Arduino as well as Pi with Gertboard, an expansion board with an onboard ATmega microcontroller.Learn Raspberry Pi Programming with Python has been fully updated in this new edition to cover the features of the new boards. You'll learn how to program in Python on your Raspberry Pi with hands-on examples and fun projects. What You'll LearnSet up your new Raspberry PiBuild unique projects across a range of interestsProgram basic functions and processes using PythonWho This Book Is ForReaders who want to learn Python on a fun platform like the Pi and pick up some electronics skills along the way. No programming or Linux skill required, but a little experience with Linux will be helpful. Readers familiar with the 1st edition will enjoy the updated information in this new edition.
R 1,302.99
Hands-On Financial Trading with Python
Build and backtest your algorithmic trading strategies to gain a true advantage in the marketKey FeaturesGet quality insights from market data, stock analysis, and create your own data visualisationsLearn how to navigate the different features in Python’s data analysis librariesStart systematically approaching quantitative research and strategy generation/backtesting in algorithmic tradingBook DescriptionCreating an effective system to automate your trading can help you achieve two of every trader’s key goals; saving time and making money. But to devise a system that will work for you, you need guidance to show you the ropes around building a system and monitoring its performance. This is where Hands-on Financial Trading with Python can give you the advantage.This practical Python book will introduce you to Python and tell you exactly why it’s the best platform for developing trading strategies. You’ll then cover quantitative analysis using Python, and learn how to build algorithmic trading strategies with Zipline using various market data sources.Using Zipline as the backtesting library allows access to complimentary US historical daily market data until 2018. As you advance, you will gain an in-depth understanding of Python libraries such as NumPy and pandas for analyzing financial datasets, and explore Matplotlib, statsmodels, and scikit-learn libraries for advanced analytics.As you progress, you’ll pick up lots of skills like time series forecasting, covering pmdarima and Facebook Prophet.By the end of this trading book, you will be able to build predictive trading signals, adopt basic and advanced algorithmic trading strategies, and perform portfolio optimization to help you get —and stay—ahead of the markets.What you will learnDiscover how quantitative analysis works by covering financial statistics and ARIMAUse core Python libraries to perform quantitative research and strategy development using real datasetsUnderstand how to access financial and economic data in PythonImplement effective data visualization with MatplotlibApply scientific computing and data visualization with popular Python librariesBuild and deploy backtesting algorithmic trading strategiesWho this book is forIf you’re a financial trader or a data analyst who wants a hands-on introduction to designing algorithmic trading strategies, then this book is for you. You don’t have to be a fully-fledged programmer to dive into this book, but knowing how to use Python’s core libraries and a solid grasp on statistics will help you get the most out of this book.
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