Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras

Ritesh Bhagwat, Mahla Abdolahnejad, Matthew Moocarme

Though designing neural networks is a sought-after skill, it is not easy to master. With Keras, you can apply complex machine learning algorithms with minimum code.Applied Deep Learning with Keras starts by taking you through the basics of machine learning and Python all the way to gaining an in-depth understanding of applying Keras to develop efficient deep learning solutions. To help you grasp the difference between machine and deep learning, the book guides you on how to build a logistic regression model, first with scikit-learn and then with Keras. You will delve into Keras and its many models by creating prediction models for various real-world scenarios, such as disease prediction and customer churning. You’ll gain knowledge on how to evaluate, optimize, and improve your models to achieve maximum information. Next, you’ll learn to evaluate your model by cross-validating it using Keras Wrapper and scikit-learn. Following this, you’ll proceed to understand how to apply L1, L2, and dropout regularization techniques to improve the accuracy of your model. To help maintain accuracy, you’ll get to grips with applying techniques including null accuracy, precision, and AUC-ROC score techniques for fine tuning your model.By the end of this book, you will have the skills you need to use Keras when building high-level deep neural networks.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices

Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

Applied Machine Learning for Healthcare and Life Sciences Using AWS. Transformational AI implementations for biotech, clinical, and healthcare organizations

Ujjwal Ratan

While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.

Applied Network Security. Proven tactics to detect and defend against all kinds of network attack

Arthur Salmon, Michael McLafferty, Warun Levesque

Computer networks are increasing at an exponential rate and the most challenging factor organisations are currently facing is network security. Breaching a network is not considered an ingenious effort anymore, so it is very important to gain expertise in securing your network.The book begins by showing you how to identify malicious network behaviour and improve your wireless security. We will teach you what network sniffing is, the various tools associated with it, and how to scan for vulnerable wireless networks. Then we’ll show you how attackers hide the payloads and bypass the victim’s antivirus. Furthermore, we’ll teach you how to spoof IP / MAC address and perform an SQL injection attack and prevent it on your website. We will create an evil twin and demonstrate how to intercept network traffic. Later, you will get familiar with Shodan and Intrusion Detection and will explore the features and tools associated with it. Toward the end, we cover tools such as Yardstick, Ubertooth, Wifi Pineapple, and Alfa used for wireless penetration testing and auditing. This book will show the tools and platform to ethically hack your own network whether it is for your business or for your personal home Wi-Fi.

Applied SOA Patterns on the Oracle Platform. Fuse together your pragmatic Oracle experience with abstract SOA patterns with this practical guide

Sergey Popov

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Supervised Learning with R. Use machine learning libraries of R to build models that solve business problems and predict future trends

Karthik Ramasubramanian, Jojo Moolayil

R provides excellent visualization features that are essential for exploring data before using it in automated learning.Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model.By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones, Christopher Kruger

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.By the end of this book, you will have the skills you need to confidently build your own models using Python.

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Applying and Extending Oracle Spatial. This guide takes you straight into the attributes of Oracle Spatial and teaches you to extend, apply, and combine them with other Oracle and open source technologies. A vital manual for solving everyday problems

Siva Ravada, Simon Greener

Spatial applications should be developed in the same way that users develop other database applications: by starting with an integrated data model in which the SDO_GEOMETRY objects are just another attribute describing entities and by using as many of the database features as possible for managing the data. If a task can be done using a database feature like replication, then it should be done using the standard replication technology instead of inventing a new procedure for replicating spatial data. Sometimes solving a business problem using a PL/SQL function can be more powerful, accessible, and easier to use than trying to use external software. Because Oracle Spatial's offerings are standards compliant, this book shows you how Oracle Spatial technology can be used to build cross-vendor database solutions. Applying and Extending Oracle Spatial shows you the clever things that can be done not just with Oracle Spatial on its own, but in combination with other database technologies. This is a great resource book that will convince you to purchase other Oracle technology books on non-spatial specialist technologies because you will finally see that spatial is not special: it is a small, fun, and clever part of a much larger whole.

Applying Artificial Intelligence in Project Management. Harness the power of AI to transform project management practices

Mercury Learning and Information, Paul Boudreau

Artificial intelligence is reshaping the way projects are managed, offering unprecedented opportunities to improve efficiency, accuracy, and outcomes. This course begins with an introduction to AI’s role in project management, exploring how machine learning, natural language processing, and predictive algorithms can transform traditional approaches. You’ll learn about the key components of AI-driven projects and how to develop a strong business case for adopting these innovations.As you progress, the course delves into practical applications of AI in automating project tasks, analyzing data, and predicting results. Participants will gain hands-on experience with tools that leverage machine learning to forecast project success, improve productivity, and resolve potential failures. Additionally, you’ll discover how generative AI and large language models can enhance communication, planning, and decision-making throughout the project lifecycle.Finally, the course examines the broader implications of integrating AI into project management. You’ll explore strategies for acquiring AI solutions, implementing them within teams, and navigating the ethical challenges they present. By the end of the course, participants will have a clear understanding of how to leverage AI to optimize projects and stay competitive in a rapidly evolving technological landscape.

Applying Math with Python. Over 70 practical recipes for solving real-world computational math problems - Second Edition

Sam Morley

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Applying Math with Python. Practical recipes for solving computational math problems using Python programming and its libraries

Sam Morley

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Apps and Services with .NET 7. Build practical projects with Blazor, .NET MAUI, gRPC, GraphQL, and other enterprise technologies

Mark J. Price

Apps and Services with .NET 7 is for .NET 6 and .NET 7 developers who want to kick their C# and .NET understanding up a gear by learning the practical skills and knowledge they need to build real-world applications and services. It covers specialized libraries that will help you monitor and improve performance, secure your data and applications, and internationalize your code and apps.With chapters that put a variety of technologies into practice, including Web API, OData, gRPC, GraphQL, SignalR, and Azure Functions, this book will give you a broader scope of knowledge than other books that often focus on only a handful of .NET technologies. It covers the latest developments, libraries, and technologies that will help keep you up to date.You’ll also leverage .NET MAUI to develop mobile apps for iOS and Android as well as desktop apps for Windows and macOS.

Apps and Services with .NET 8. Build practical projects with Blazor, .NET MAUI, gRPC, GraphQL, and other enterprise technologies - Second Edition

Mark J. Price

Elevate your practical C# and .NET skills to the next level with this new edition of Apps and Services with .NET 8.With chapters that put a variety of technologies into practice, including Web API, gRPC, GraphQL, and SignalR, this book will give you a broader scope of knowledge than other books that often focus on only a handful of .NET technologies. You’ll dive into the new unified model for Blazor Full Stack and leverage .NET MAUI to develop mobile and desktop apps.This new edition introduces the latest enhancements, including the seamless implementation of web services with ADO.NET SqlClient's native Ahead-of-Time (AOT) support. Popular library coverage now includes Humanizer and Noda Time. There’s also a brand-new chapter that delves into service architecture, caching, queuing, and robust background services.By the end of this book, you’ll have a wide range of best practices and deep insights under your belt to help you build rich apps and efficient services.

Apteka dla duszy. Zbiór medytacji i ćwiczeń relaksacyjnych

Osho

Poznaj siebie, zrozum własne ciało i umysł, a odnajdziesz drogę do innych. Kolejna kultowa książka jednego z największych przewodników duchowych współczesności. Dowiesz się z niej, jakie korzyści przynosi medytacja. Osho uzmysłowi Ci, że aby docenić raj, najpierw trzeba go utracić. By umysł mógł dostrzec istotę absolutnego piękna, potrzebna jest mu cisza. Umysł trwający w stanie medytacji, jest w stanie odnaleźć nieskończoną krainę wieczności. Znajduje wszystko to, co można nazwać błogosławieństwem. Medytacja jest poszukiwaniem porozumienia z samym sobą. Jeżeli nie nauczysz się wsłuchiwać w swoje pragnienia, nie będziesz umiał porozumieć się z innymi.

Apteka Pana Boga

Maria Treben

W czasach, gdy większość ludzi odchodzi od naturalnego trybu życia i gdy z powodu złego nastawienia do rzeczywistości dopadają nas groźne choroby, powinniśmy wrócić do ziół leczniczych, które Bóg w swej dobroci ofiarowuje nam od zarania dziejów. Maria Treben zajęła w historii miejsce jednej z najważniejszych pionierek ziołolecznictwa. Przez wielu czczona niemal jak święta, w rzeczywistości była kobietą, która przez całe życie zajmowała się ziołami leczniczymi i jedyne, czego pragnęła, to dzielić się swoimi doświadczeniami z jak największą liczbą osób. Z biegiem lat popularność jej samej i jej dorobku nie straciła na znaczeniu. Wręcz przeciwnie, dzięki rosnącemu zainteresowaniu medycyną alternatywną napisane przez nią książki ponownie stają się aktualne.   W swoim głównym dziele Apteka Pana Boga szczegółowo opisała właściwości terapeutyczne 31 ziół leczniczych. Przedstawiła możliwości ich przyjmowania w postaci herbat, nalewek, świeżego soku, a także stosowania jako składników okładów leczniczych oraz dodatków do kąpieli. Apteka Pana Boga ukazała się w następujących językach: angielskim, bośniackim, bułgarskim, chorwackim, czeskim, duńskim, estońskim, fińskim, francuskim, greckim, hiszpańskim, japońskim, litewskim, łotewskim, niderlandzkim, niemieckim, norweskim, polskim, portugalskim, rosyjskim, rumuńskim, serbskim, słowackim, słoweńskim, szwedzkim, tureckim, węgierskim, włoskim  

Apteka pod Złotym Moździerzem. Dziedzictwo

Lucyna Olejniczak

Drugi tom opowieści o przodkach bohaterów sagi "Kobiety z ulicy Grodzkiej". Korzenie zła i narodziny niewieściej siły w bezwzględnych czasach walki o życie, nadzieję i marzenia. Rok 1849. Podczas podroży poślubnej Magda spotyka kobietę, która przepowiada jej, że młoda doktorowa osiągnie swój cel, ale nie będzie szczęśliwa. Na razie jednak wszystkie marzenia się spełniają. Magda ma kochającego męża i własną aptekę. Małżonkowie podejmują jeszcze jedną ważną decyzję, która, jak się okaże, odmieni na zawsze ich życie. Mijają kolejne dekady, nad głowami Bernatów zaczynają zbierać się ciemne chmury. W mieście wybucha straszliwy pożar, a potem wszyscy zostają uwikłani w wydarzenia powstania styczniowego. Ale najgorsze dopiero przed nimi. Marzenia o szczęśliwej rodzinie zostaną poddane poważnej próbie. Czyżby przepowiednia sprzed lat miała się spełnić? Lucyna Olejniczak urodziła się i mieszka w Krakowie. Z zawodu laborantka medyczna, były pracownik Katedry Farmakologii UJ, jako pisarka zadebiutowała już na emeryturze. Dużo podróżuje, lubi poznawać nowych ludzi, łatwo się zaprzyjaźnia. Wielbicielka kotów i wnuczki. Autorka m.in. bestellerowej sagi "Kobiety z ulicy Grodzkiej".

Apteka pod Złotym Moździerzem.Obca

Lucyna Olejniczak

Pierwszy z dwóch tomów opowieści o protoplastach bohaterów sagi "Kobiety z ulicy Grodzkiej". Korzenie zła i narodziny niewieściej siły w bezwzględnych czasach walki o życie, nadzieję i marzenia. W 1831 roku w Galicji wybucha epidemia cholery. Z ogarniętej zarazą wioski ucieka młoda kobieta w ciąży. Kilkanaście lat później jej córka, Magda, urodzona i wychowana w klasztorze benedyktynek, przybywa do Krakowa, aby tam rozpocząć dorosłe życie. Odważna i spragniona wiedzy dziewczyna jest wszędzie, gdzie coś się dzieje i gdzie czuje się potrzebna. Walczy na barykadzie podczas powstania krakowskiego, przeżywa pierwszą miłość i wkrótce znajduje swe życiowe powołanie - chce pomagać chorym. Mimo wielu przeciwności losu nie poddaje się, marząc o własnej aptece. Wie, że jako kobieta ma niewielkie szanse na spełnienie swoich pragnień, ale tym razem szczęście wydaje się jej sprzyjać. Czy jednak nie będzie musiała za to zapłacić wysokiej ceny? Lucyna Olejniczak urodziła się i mieszka w Krakowie. Z zawodu laborantka medyczna, były pracownik Katedry Farmakologii UJ, jako pisarka zadebiutowała już na emeryturze. Dużo podróżuje, lubi poznawać nowych ludzi, łatwo się zaprzyjaźnia. Wielbicielka kotów i wnuczki.

Apteka w getcie krakowskim

Tadeusz Pankiewicz

Historia krakowskiego getta opowiedziana przez polskiego aptekarza. Tadeusz Pankiewicz z okien swojej apteki, znajdującej się w sercu krakowskiego getta, widział rozgrywającą się tragedię. Zagłada Żydów pochłonęła życie jego przyjaciół i sąsiadów. Spisując wiernie dzieje getta, udało mu się uratować ich przed zapomnieniem. Apteka Pod Orłem była jedyną, która funkcjonowała w utworzonym w 1941 roku getcie, a Pankiewicz był jedynym Polakiem z prawem stałego przebywania w nim. W tym miejscu nie tylko sprzedawano leki szybko zaczęło ono pełnić rolę azylu i punktu kontaktowego dwu światów: zamkniętej w getcie społeczności żydowskiej i ludzi poza jego murami, cieszących się okupacyjną, ale jednak swobodą. Tu można było przeczytać ostatnie wiadomości z frontu, zapoznać się z treścią prasy podziemnej, a nawet znaleźć schronienie podczas nocnych aresztowań. Tu również zostawiano listy i paczki dla osób mieszkających po stronie aryjskiej, jak również odbierano od nich wiadomości i przesyłki. Książkę Tadeusza Pankiewicza, bezcenne i wzruszające świadectwo tamtych dni, opublikowano po raz pierwszy w 1947 roku. Nieliczni mieszkańcy getta, którzy zdołali przeżyć opisywany przez aptekarza okrutny czas wojny, zachowali do dziś we wdzięcznej pamięci aptekę Pod Orłem i jej właściciela.

Aptekarka. Saga Rodu Tyszkowskich

Magda Skubisz

Katja jest zielarką. Najlepszą w okolicy, a może i całej Galicji. Rośliny do niej mówią (nie naprawdę, rośliny przecież nie potrafią mówić), dziewczyna ma instynkt medyczny, a od romansów woli zielniki. I to jej przysparza kłopotów, bo w XIX wieku nawet lekarz nie potrafi docenić wiedzy i logiki. Gdy do tego dodać charakterek i cięty język, łatwo sobie wyobrazić, przez co Katja przechodzi na pańskich dworach albo w chałupach chorych, gdzie rządzi szeptucha. Żeby było trudniej, dziewczyna odebrała wyprzedzające epokę feministyczne wychowanie od przybranego ojca, zielarza Batki, który na stronach poradników dla dobrze wychowanych panien zwykł suszyć grzyby. Słowem: Katja często musi w duchu odmawiać modlitwę do patronki cierpliwych, żeby nie trafił ją zwykły, ludzki szlag. A ten trafia ją najczęściej w towarzystwie pewnego zadufanego, złośliwego i przystojnego szlachcica. Jego dowcip jest równie ostry jak jej inteligencja, a jej zadziorny charakter równie twardy co jego wojskowe obycie. Jaki los czeka biedną dziewczynę w takim świecie? Aptekarka, pierwszy tom serii, to znakomita powieść historyczno-obyczajowa Magdy Skubisz, autorki nominowanej do Literackiej Nagrody im. Józefa Mackiewicza za debiut LO Story. Zastrzeżenie: niektóre postacie występujące w książce nie są fikcyjne i można je spokojnie mylić z historycznymi, a zawarte w książce treści medyczno-zielarskie nie zastępują fachowej porady lekarskiej, choć mogą pomóc. Opowieść o silnej kobiecie, która jest świadoma swojej wiedzy i mocy. W zielarce Katji skupia się tragedia zdolnych kobiet w czasach, kiedy musiały przepraszać za swoją wiedzę, prosić o pozwolenie na praktykę czy przekonywać mężczyzn, że są równie dobre jak oni. Bardzo polecam. Ałbena Grabowska - pisarka i lekarka