Biznes IT
Giuseppe Ciaburro, Prateek Joshi
This eagerly anticipated second edition of the popular Python Machine Learning Cookbook will enable you to adopt a fresh approach to dealing with real-world machine learning and deep learning tasks.With the help of over 100 recipes, you will learn to build powerful machine learning applications using modern libraries from the Python ecosystem. The book will also guide you on how to implement various machine learning algorithms for classification, clustering, and recommendation engines, using a recipe-based approach. With emphasis on practical solutions, dedicated sections in the book will help you to apply supervised and unsupervised learning techniques to real-world problems. Toward the concluding chapters, you will get to grips with recipes that teach you advanced techniques including reinforcement learning, deep neural networks, and automated machine learning.By the end of this book, you will be equipped with the skills you need to apply machine learning techniques and leverage the full capabilities of the Python ecosystem through real-world examples.
Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III
Sebastian Raschka, Vahid Mirjalili
Uczenie maszynowe jest jedną z najbardziej fascynujących technologii naszych czasów - rozwojem jego najróżniejszych zastosowań zajmują się tacy giganci jak Google, Facebook, Apple, Amazon czy IBM. Uczenie maszynowe otwiera zupełnie nowe możliwości i powoli staje się nieodzowne: wystarczy wymienić asystenty głosowe w smartfonach, chatboty ułatwiające klientom wybór produktu, a także sieci ułatwiające podejmowanie decyzji o inwestycjach giełdowych, filtrujące niechciane wiadomości e-mail czy wspomagające diagnostykę medyczną. Oto obszerny przewodnik po uczeniu maszynowym i uczeniu głębokim w Pythonie. Zawiera dokładne omówienie najważniejszych technik uczenia maszynowego oraz staranne wyjaśnienie zasad rządzących tą technologią. Poszczególne zagadnienia zilustrowano mnóstwem wyjaśnień, wizualizacji i przykładów, co znakomicie ułatwia zrozumienie materiału i sprawne rozpoczęcie samodzielnego budowania aplikacji i modeli, takich jak te służące do klasyfikacji obrazów, odkrywania ukrytych wzorców czy wydobywania dodatkowych informacji z danych. Wydanie trzecie zostało zaktualizowane - znalazł się w nim opis biblioteki TensorFlow 2 i najnowszych dodatków do biblioteki scikit-learn. Dodano również wprowadzenie do dwóch nowatorskich technik: uczenia przez wzmacnianie i budowy generatywnych sieci przeciwstawnych (GAN). W książce między innymi: platformy, modele i techniki uczenia maszynowego wykorzystywanie biblioteki scikit-learn i TensorFlow sieci neuronowe, sieci GAN i inne przygotowywanie danych dla modeli uczenia maszynowego ocena i strojenie modeli analizy: regresyjna, skupień i sentymentów Uczenie głębokie z Pythonem: zrozum i zastosuj!
Sebastian Raschka, Vahid Mirjalili
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Sebastian Raschka, Vahid Mirjalili
Publisher's Note: This edition from 2017 is outdated and is not compatible with TensorFlow 2 or any of the most recent updates to Python libraries. A new third edition, updated for 2020 and featuring TensorFlow 2 and the latest in scikit-learn, reinforcement learning, and GANs, has now been published.Machine learning is eating the software world, and now deep learning is extending machine learning. Understand and work at the cutting edge of machine learning, neural networks, and deep learning with this second edition of Sebastian Raschka’s bestselling book, Python Machine Learning. Using Python's open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis.Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow 1.x deep learning library. The scikit-learn code has also been fully updated to v0.18.1 to include improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili’s unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples. By the end of the book, you’ll be ready to meet the new data analysis opportunities.If you’ve read the first edition of this book, you’ll be delighted to find a balance of classical ideas and modern insights into machine learning. Every chapter has been critically updated, and there are new chapters on key technologies. You’ll be able to learn and work with TensorFlow 1.x more deeply than ever before, and get essential coverage of the Keras neural network library, along with updates to scikit-learn 0.18.1.
Sebastian Raschka, Vahid Mirjalili
Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems.Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents.This book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.
Jalaj Thanaki
This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data.By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world.
Zhenya Antić, Saurabh Chakravarty, Edward A. Fox
Harness the power of Natural Language Processing (NLP) to overcome real-world text analysis challenges with this recipe-based roadmap written by two seasoned NLP experts with vast experience transforming various industries with their NLP prowess.You’ll be able to make the most of the latest NLP advancements, including large language models (LLMs), and leverage their capabilities through Hugging Face transformers. Through a series of hands-on recipes, you’ll master essential techniques such as extracting entities and visualizing text data. The authors will expertly guide you through building pipelines for sentiment analysis, topic modeling, and question-answering using popular libraries like spaCy, Gensim, and NLTK. You’ll also learn to implement RAG pipelines to draw out precise answers from a text corpus using LLMs.This second edition expands your skillset with new chapters on cutting-edge LLMs like GPT-4, Natural Language Understanding (NLU), and Explainable AI (XAI)—fostering trust in your NLP models.By the end of this book, you'll be equipped with the skills to apply advanced text processing techniques, use pre-trained transformer models, build custom NLP pipelines to extract valuable insights from text data to drive informed decision-making.
Python. Podstawy nauki o danych. Wydanie II
Alberto Boschetti, Luca Massaron
Nauka o danych jest nową, interdyscyplinarną dziedziną, funkcjonującą na pograniczu algebry liniowej, modelowania statystycznego, lingwistyki komputerowej, uczenia maszynowego oraz metod akumulacji danych. Jest przydatna między innymi dla analityków biznesowych, statystyków, architektów oprogramowania i osób zajmujących się sztuczną inteligencją. Szczególnie praktycznym narzędziem dla tych specjalistów jest język Python, który zapewnia doskonałe środowisko do analizy danych, uczenia maszynowego i algorytmicznego rozwiązywania problemów. Niniejsza książka jest doskonałym wprowadzeniem do nauki o danych. Jej autorzy wskażą Ci prostą i szybką drogę do rozwiązywania różnych problemów z tego obszaru za pomocą Pythona oraz powiązanych z nim pakietów do analizy danych i uczenia maszynowego. Dzięki lekturze przejdziesz przez kolejne etapy modyfikowania i wstępnego przetwarzania danych, poznając przy tym podstawowe operacje związane z wczytywaniem danych, przekształcaniem ich, poprawianiem na potrzeby analiz, eksplorowaniem i przetwarzaniem. Poza podstawami opanujesz też zagadnienia uczenia maszynowego, w tym uczenia głębokiego, techniki analizy grafów oraz wizualizacji danych. Najważniejsze zagadnienia przedstawione w książce: konfiguracja środowiska Jupyter Notebook najważniejsze operacje stosowane w nauce o danych potoki danych i uczenie maszynowe wprowadzenie do grafów i wizualizacje biblioteki i pakiety Pythona służące do badań danych Nauka o danych — fascynujące algorytmy i potężne grafy! Alberto Boschetti specjalizuje się w przetwarzaniu sygnałów i statystyce. Jest doktorem inżynierii telekomunikacyjnej. Zajmuje się przetwarzaniem języków naturalnych, analityką behawioralną, uczeniem maszynowym i przetwarzaniem rozproszonym. Luca Massaron specjalizuje się w statystycznych analizach wieloczynnikowych, uczeniu maszynowym, statystyce, eksploracji danych i algorytmice. Pasjonuje się potencjałem, jaki drzemie w nauce o danych.
Sean Saito, Yang Wenzhuo, Rajalingappaa Shanmugamani
Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you'll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.