Biznes IT
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, Andrei Lopatenko
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.
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.
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.