Biznes IT
Cuantum Technologies LLC
Embark on a comprehensive journey to master natural language processing (NLP) with Python. Begin with foundational concepts like text preprocessing, tokenization, and key Python libraries such as NLTK, spaCy, and TextBlob. Explore the challenges of text data and gain hands-on experience in cleaning, tokenizing, and building basic NLP pipelines. Early chapters provide practical exercises to solidify your understanding of essential techniques.Advance to sophisticated topics like feature engineering using Bag of Words, TF-IDF, and embeddings like Word2Vec and BERT. Delve into language modeling with RNNs, syntax parsing, and sentiment analysis, learning to apply these techniques in real-world scenarios. Chapters on topic modeling and text summarization equip you to extract insights from data, while transformer-based models like BERT take your skills to the next level. Each concept is paired with Python-based examples, ensuring practical mastery.The final chapters focus on real-world projects, such as developing chatbots, sentiment analysis dashboards, and news aggregators. These hands-on applications challenge you to design, train, and deploy robust NLP solutions. With its structured approach and practical focus, this book equips you to confidently tackle real-world NLP challenges and innovate in the field.
Nirant Kasliwal
NLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP.The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a work?ow for building NLP applications.We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn.We conclude by deploying these models as REST APIs with Flask.By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges.
Thushan Ganegedara
Natural language processing (NLP) supplies the majority of data available to deep learning applications, while TensorFlow is the most important deep learning framework currently available. Natural Language Processing with TensorFlow brings TensorFlow and NLP together to give you invaluable tools to work with the immense volume of unstructured data in today’s data streams, and apply these tools to specific NLP tasks.Thushan Ganegedara starts by giving you a grounding in NLP and TensorFlow basics. You'll then learn how to use Word2vec, including advanced extensions, to create word embeddings that turn sequences of words into vectors accessible to deep learning algorithms. Chapters on classical deep learning algorithms, like convolutional neural networks (CNN) and recurrent neural networks (RNN), demonstrate important NLP tasks as sentence classification and language generation. You will learn how to apply high-performance RNN models, like long short-term memory (LSTM) cells, to NLP tasks. You will also explore neural machine translation and implement a neural machine translator.After reading this book, you will gain an understanding of NLP and you'll have the skills to apply TensorFlow in deep learning NLP applications, and how to perform specific NLP tasks.
Thushan Ganegedara
Learning how to solve natural language processing (NLP) problems is an important skill to master due to the explosive growth of data combined with the demand for machine learning solutions in production. Natural Language Processing with TensorFlow, Second Edition, will teach you how to solve common real-world NLP problems with a variety of deep learning model architectures.The book starts by getting readers familiar with NLP and the basics of TensorFlow. Then, it gradually teaches you different facets of TensorFlow 2.x. In the following chapters, you then learn how to generate powerful word vectors, classify text, generate new text, and generate image captions, among other exciting use-cases of real-world NLP.TensorFlow has evolved to be an ecosystem that supports a machine learning workflow through ingesting and transforming data, building models, monitoring, and productionization. We will then read text directly from files and perform the required transformations through a TensorFlow data pipeline. We will also see how to use a versatile visualization tool known as TensorBoard to visualize our models.By the end of this NLP book, you will be comfortable with using TensorFlow to build deep learning models with many different architectures, and efficiently ingest data using TensorFlow Additionally, you’ll be able to confidently use TensorFlow throughout your machine learning workflow.
Nawyk produktywności. Wszystko, czego potrzebujesz, aby osiągać w życiu maksymalne efekty
Agnieszka Jarzębowska
Co masz zrobić jutro, zrób dziś. A jutro... zrealizuj kolejne zadanie z listy! Jak wiele razy składałeś deklaracje, że coś zmienisz w swoim życiu, że zaczniesz ćwiczyć, poszukasz innej posady, że będziesz częściej spędzać czas z rodziną, zamiast trwonić go w internecie lub przed telewizorem? Ile razy, siedząc w biurze, mimo że wiedziałeś, jakie masz priorytety, szukałeś zadań zastępczych, prostych i w gruncie rzeczy nieistotnych? Ile razy musiałeś przez to zabierać robotę do domu albo odsiadywać nadgodziny? Jeśli miałeś okazję doświadczyć którejś z wymienionych (albo podobnej) sytuacji, to jesteś osobą, do której adresowana jest ta książka - wiesz, ile stresu przysparza nadmiar zadań, gonitwa za dotrzymaniem terminów i chaotyczne próby uporania się ze wszystkim, co jest do zrobienia. Skąd się biorą Twoje problemy? Najprawdopodobniej ich źródło tkwi w złych nawykach: odkładaniu na później (prokrastynacji), braniu na siebie zbyt wielu zadań i obowiązków, działaniu bez celu i planu, rozpraszaniu się, niedosypianiu i braku przerw w czasie pracy. Na szczęście Twoje nawyki nie są Tobą - są tym, czego się w życiu nauczyłeś poprzez powtarzanie. I w ten sam sposób możesz nauczyć się innych, produktywnych nawyków, które pomogą Ci dokańczać rozpoczęte sprawy, realizować naprawdę ważne zadania i zbalansować wreszcie czas, jaki poświęcasz na pracę, z czasem przeznaczonym na odpoczynek. Produktywne nawyki, o których przeczytasz w tej książce, nie są trudne do opanowania. Może je łatwo wdrożyć każdy, pod warunkiem że będzie odpowiednio zdeterminowany i przede wszystkim konsekwentny w działaniu. Warto to zrobić już dziś, by stać się człowiekiem sukcesu albo po prostu tym, kim zawsze chciało się być.
Nawyk wytrwałości. Jak go wykształcić metodą małych kroków
Anna Kuraszyńska
Czy wciąż brakuje Ci wytrwałości, by osiągnąć to, czego pragniesz? Jeżeli tak - ta książka jest dla Ciebie. Dowiesz się z niej, jak małymi krokami zrealizować swoje cele: wyjść z nałogu osiągnąć idealne ciało zdobyć wymarzoną pracę stworzyć szczęśliwe związki z ludźmi Metoda małych kroków opisana w tej książce opiera się na założeniu, że Twój cel może być wielki, ale kroki prowadzące do niego powinny być małe i tak rozłożone w czasie, byś niemal nie odczuwał, że cokolwiek nowego dzieje się w Twoim życiu. W tym tkwi cały sekret. Pierwszy krok powinien być tak mały, że wręcz powinieneś odczuwać niedosyt działania. Na początku powinno być łatwo! Kolejne kroki powinny być równie małe. Jak wykształcić w sobie nawyk wytrwałości ? Oto 10 złotych zasad. Gdy wyznaczasz swoje cele, kieruj się miłością do świata i innych. Zapisuj cele i zadania w notesie lub kalendarzu. Rozbijaj cele na drobne elementy i rozkładaj je w czasie. Zaczynaj zawsze od łatwego zadania i powoli posuwaj się do przodu. Wyznaczaj czas na wykonanie określonych zadań. Koncentruj się tylko na jednym zadaniu. Wystrzegaj się robienia wyjątków. Stosuj techniki motywacyjne. Gdy nie chce ci się czegoś zrobić, wstań i to zrób. Naucz się dobrze żyć z odrzuceniem i problemami. Doceniaj się za robienie rzeczy małych, doceniaj się za robienie rzeczy małych, doceniaj się za robienie rzeczy małych. W tym tkwi sekret wielkich osiągnięć! Z pamiętnika autorki Anna Kuraszyńska ukończyła psychologię stosowaną na Uniwersytecie Jagiellońskim. W pracy zawodowej opiera się na nurcie behawioralno-poznawczym i humanistycznym. Propaguje strategię małych kroków, by pomóc ludziom podnieść jakość życia i wydobyć pełny potencjał.
Peter Ries
NetSuite For Consultants takes a hands-on approach to help ERP and CRM consultants implement NetSuite quickly and efficiently, as well deepen their understanding of its implementation methodology.During the course of this book, you’ll get a clear picture of what NetSuite is, how it works, and how accounts, support, and updates work within its ecosystem.Understanding what a business needs is a critical first step toward completing any software product implementation, so you'll learn how to write business requirements by learning about the various departments, roles, and processes in the client's organization. Once you've developed a solid understanding of NetSuite and your client, you’ll be able to apply your knowledge to configure accounts and test everything with the users. You’ll also learn how to manage both functional and technical issues that arise post-implementation and handle them like a professional.By the end of this book, you'll have gained the necessary skills and knowledge to implement NetSuite for businesses and get things up and running in the shortest possible time.
Peter Ries
NetSuite consultants face increasingly complex challenges in multi-currency configurations, financial automation, and third-party integrations that become maintenance nightmares. Add evolving client expectations and rapidly advancing platform capabilities, and many consultants find themselves struggling to deliver the transformative results their clients demand.This comprehensive guide provides the up-to-date roadmap you need to master NetSuite implementation and customization, turning these persistent challenges into competitive advantages.In this updated edition, author Peter Ries, an Oracle NetSuite Consulting Technical Director, expands on the latest advancements, including AI-enabled features, SuiteAnalytics, mobile apps, and new integration options. You'll gain access to proven consulting strategies, ERP optimization best practices, and practical guidance for configuring NetSuite to meet complex business requirements. Through real-world scenarios and actionable case studies, you’ll learn how to navigate common pitfalls and implement scalable solutions that drive long-term success.By the end of this book, you’ll be fully prepared to streamline NetSuite implementations and deliver high-impact consulting services confidently. It’s designed to maximize your career potential, no matter your level of experience.
Manpreet Singh Ghotra, Rajdeep Dua
If you're aware of the buzz surrounding the terms such as machine learning, artificial intelligence, or deep learning, you might know what neural networks are. Ever wondered how they help in solving complex computational problem efficiently, or how to train efficient neural networks? This book will teach you just that.You will start by getting a quick overview of the popular TensorFlow library and how it is used to train different neural networks. You will get a thorough understanding of the fundamentals and basic math for neural networks and why TensorFlow is a popular choice Then, you will proceed to implement a simple feed forward neural network. Next you will master optimization techniques and algorithms for neural networks using TensorFlow. Further, you will learn to implement some more complex types of neural networks such as convolutional neural networks, recurrent neural networks, and Deep Belief Networks. In the course of the book, you will be working on real-world datasets to get a hands-on understanding of neural network programming. You will also get to train generative models and will learn the applications of autoencoders.By the end of this book, you will have a fair understanding of how you can leverage the power of TensorFlow to train neural networks of varying complexities, without any hassle. While you are learning about various neural network implementations you will learn the underlying mathematics and linear algebra and how they map to the appropriate TensorFlow constructs.
James Loy
Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch.By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.
V Kishore Ayyadevara
This book will take you from the basics of neural networks to advanced implementations of architectures using a recipe-based approach.We will learn about how neural networks work and the impact of various hyper parameters on a network's accuracy along with leveraging neural networks for structured and unstructured data.Later, we will learn how to classify and detect objects in images. We will also learn to use transfer learning for multiple applications, including a self-driving car using Convolutional Neural Networks.We will generate images while leveraging GANs and also by performing image encoding. Additionally, we will perform text analysis using word vector based techniques. Later, we will use Recurrent Neural Networks and LSTM to implement chatbot and Machine Translation systems.Finally, you will learn about transcribing images, audio, and generating captions and also use Deep Q-learning to build an agent that plays Space Invaders game.By the end of this book, you will have developed the skills to choose and customize multiple neural network architectures for various deep learning problems you might encounter.
Neural Networks with R. Build smart systems by implementing popular deep learning models in R
Balaji Venkateswaran, Giuseppe Ciaburro
Neural networks are one of the most fascinating machine learning models for solving complex computational problems efficiently. Neural networks are used to solve wide range of problems in different areas of AI and machine learning.This book explains the niche aspects of neural networking and provides you with foundation to get started with advanced topics. The book begins with neural network design using the neural net package, then you’ll build a solid foundation knowledge of how a neural network learns from data, and the principles behind it. This book covers various types of neural network including recurrent neural networks and convoluted neural networks. You will not only learn how to train neural networks, but will also explore generalization of these networks. Later we will delve into combining different neural network models and work with the real-world use cases.By the end of this book, you will learn to implement neural network models in your applications with the help of practical examples in the book.
Tomasz Rymarczyk Krzysztof Król
This publication was prepared as a permanent record of research findings presented by doctoral students and academics representing diverse research backgrounds and schools. The focus is on interdisciplinary solutions, combining engineering, computer science, and mathematical methods with approaches relevant to management science and socio-economic applications. The International Interdisciplinary PhD Workshop (IIPhDW) is a cyclical and international conference, and its primary function is to provide a platform for research presentations, knowledge exchange, and collaboration between young scientists from various disciplines. The 2025 edition featured a particularly strong technical component, encompassing artificial intelligence, computer science, automation and control, robotics and mechatronics, telecommunications, signal processing, as well as mechanical and production engineering. At the same time, the inclusion of topics in economics and management confirmed the workshop's broad scope and its ability to integrate research perspectives relevant to contemporary technological and organizational challenges. A significant group of publications includes works on process and biomedical tomography, as well as image reconstruction methods using machine learning and deep learning, including approaches combining physical models with neural network architectures. Examples include research on image reconstruction in electrical impedance tomography, the integration of tomography with neural networks for industrial process monitoring, and the use of ultrasound tomography in measurement and reconstruction analysis. This theme is further reinforced by works on hybrid tomography systems, process monitoring using mixed reality technology, and applications in the areas of physiological parameter monitoring and non-invasive diagnostics. The second recognizable axis is artificial intelligence in IT and cyberphysical systems, encompassing both the construction of predictive and classification models and their implementation in industrial, medical, and service environments. This trend includes work related to the application of machine learning methods in system and network security, solutions based on LLM agents in project team workflows, processing unstructured data using OCR and language models, and multimodal analysis in intelligent customer service systems. This perspective highlights the contemporary trend of convergence of AI techniques with data engineering, software engineering, and systems integration, which has direct implications for the design of scalable implementation solutions. The third thematic area covers embedded systems, communication, and signal processing, along with elements of computational resource optimization. The monograph includes papers on, among other things, phase shift estimation in noisy environments, pseudorandom sequence generation, acoustic feature detection, and the efficiency of machine learning applications at the network edge in the context of Kubernetes scheduling heuristics. In the monograph, this strand serves a methodological purpose, providing signal analysis tools and computational mechanisms that form the foundation for many AI and measurement system applications. A significant complement to the technical perspective are works in the areas of management and organizational and economic analysis, which address the need to understand the determinants of technology implementation and the conduct of innovative projects. The publications address, among other things, the predictors of success in startup management and the analysis of organizational improvements in public institutions. Their presence strengthens the interdisciplinary nature of the monograph by demonstrating that the effectiveness of engineering solutions depends not only on the quality of algorithms and devices, but also on the organizational, process, and decision-making context. The monograph is intended as a reference for academics and doctoral students, particularly those seeking examples of research that combines theory with application. The collected papers offer a comprehensive overview of research activities typical of early careers in science, from conceptual studies and method analysis, through device and software architecture prototyping, to experiments and evaluation of the effectiveness of proposed solutions. At the same time, the publication allows for the identification of common methodological denominators, such as the growing importance of measurement data, simulation, deep learning, systems integration, and the pursuit of real-time operation in industrial and biomedical environments. The introduction, on the one hand, contextualizes the monograph within the mission of IIPhDW as a workshop supporting researcher development and the internationalization of research. On the other hand, it organizes the chapter topics in the perspective of dominant technological trends and application needs that permeate various fields. Consequently, the monograph can be viewed as a synthetic overview of current research directions for doctoral students and young academics, as well as an inspiration for undertaking work combining artificial intelligence methods, measurement systems, software engineering, and management analyses within modern interdisciplinary projects.
Mercury Learning and Information, Derek Raine
Newtonian mechanics is fundamental in physics education due to its intellectual significance, diverse applications, and its role in teaching modeling and problem-solving. This text covers both introductory and advanced topics, making it suitable for extended study. Emphasizing problem-solving, it guides readers through the process of constructing models and finding solutions, thus enhancing their analytical skills.Starting with mechanical models and forces, the course progresses through kinematics, energy, and motion, providing a solid foundation. Further chapters delve into momentum, orbital motion, and oscillations, offering insights into dynamic systems. Advanced topics like rigid bodies, stability of motion, and Lagrangian and Hamiltonian mechanics ensure a comprehensive understanding.The journey through this course equips learners with the skills to approach complex problems, construct effective models, and develop robust solutions, making it invaluable for students aiming to excel in physics and related fields.
Dave West, Kurt Bittner, Patricia Kong
Popraw i przyspiesz dostarczanie oprogramowania w dużych, rozproszonych i złożonych projektach Nexus to najprostsze i najskuteczniejsze podejście do stosowania Scruma w skali obejmującej wiele zespołów, lokalizacji i stref czasowych. Został utworzony przez Scrum.org pionierską organizację prowadzącą szkolenia i przyznającą certyfikaty dotyczące Scruma. Organizację tę założył Ken Schwaber, współtwórca Scrum. Podczas tworzenia Nexusa wykorzystano dekady doświadczenia, stawiając czoła wyjątkowym wyzwaniom, z którymi mierzą się zespoły, łącząc się, współdzieląc pracę, zarządzając i minimalizując zależności. Nexus, czyli skalowalny Scrum to zwięzła książka, która pokazuje, jak Nexus pomaga zespołom dostarczać złożone, wieloplatformowe, programistyczne produkty w krótkich, częstych cyklach, nie poświęcając spójności ani jakości, nie dodając niepotrzebnej złożoności i nie odchodząc od zasadniczych założeń Scrum. W rozbudowanym studium przypadku autorzy zilustrowali, jak Nexus pomaga rozwiązać typowe wyzwania związane ze skalowaniem, w tym redukcję zależności między zespołami, zachowanie samoorganizacji zespołów i utrzymanie przejrzystości, ta także sprawozdawczość. Zrozum wyzwania dotyczące dostarczania pracy, zintegrowanych przyrostów produktu pochodzących od wielu zespołów oraz dowiedz się, jak odnosi się do nich Nexus. Utwórz Nexus wokół nowego lub istniejącego produktu i naucz się definiować cele i planować pracę zgodnie z Nexusem. Uruchom sprinty w Nexusie, zapewniając przejrzystość postępu, prowadząc skuteczne przeglądy sprintów Nexusa i używając retrospektyw sprintów Nexusa w celu ciągłego udoskonalania. Pokonaj wyzwania związane ze współpracą rozproszonych zespołów.
Nie wróżymy z fusów! Data driven business, czyli badania w biznesie i marketingu
Anna Karcz-Czajkowska
Fakty i dane, nie anegdoty i przeczucia Prowadzenie biznesu bez oparcia go na danych pozyskanych z rynku, zweryfikowanych i odpowiednio przetworzonych dla własnych potrzeb jest niczym więcej niż jedynie chciejstwem. Wróżeniem z fusów. Żadna firma, ani duża, ani mała, nie powinna bazować na przykładach anegdotycznych i intuicjach, ponieważ te raz się sprawdzają, a raz nie. Każdą z nich warto dla odmiany wesprzeć solidnym fundamentem badań prowadzonych w takich obszarach jak biznes i marketing. Są one narzędziem, które pomaga zrozumieć związki przyczynowo-skutkowe pomiędzy decyzjami i uwalnia je od przypadkowości. Ten przewodnik, wprowadzający w tematykę badań w biznesie głównie badań rynku i marketingowych jest skierowany do przedsiębiorców, marketerów, projektantów UX i wszystkich osób dokonujących istotnych wyborów w ramach działalności biznesowej. Czytelnik książki znajdzie w niej najważniejsze informacje dotyczące zasad, metod i narzędzi badawczych, a także podejścia empirycznego, data driven i evidence based. Zapozna się również z zagadnieniem wprowadzania w życie filozofii stawiania pytań i weryfikowania hipotez jako podstawy efektywnego zarządzania firmą.