[Télécharger] Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition de Sebastian Raschka Livres En Ligne
Télécharger Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition de Sebastian Raschka En Ligne
Télécharger "Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition" de Sebastian Raschka Livres En Ligne
Auteur : Sebastian Raschka
Catégorie : Livres anglais et étrangers,Computers & Internet,Programming
Broché : * pages
Éditeur : *
Langue : Français, Anglais
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning.Key FeaturesThird edition of the bestselling, widely acclaimed Python machine learning bookClear and intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practicesBook DescriptionPython 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.What you will learnMaster the frameworks, models, and techniques that enable machines to 'learn' from dataUse scikit-learn for machine learning and TensorFlow for deep learningApply machine learning to image classification, sentiment analysis, intelligent web applications, and moreBuild and train neural networks, GANs, and other modelsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho This Book Is ForIf you know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.Table of ContentsGiving Computers the Ability to Learn from DataTraining Simple ML Algorithms for ClassificationML Classifiers Using scikit-learnBuilding Good Training Datasets - Data PreprocessingCompressing Data via Dimensionality ReductionBest Practices for Model Evaluation and Hyperparameter TuningCombining Different Models for Ensemble LearningApplying ML to Sentiment AnalysisEmbedding a ML Model into a Web ApplicationPredicting Continuous Target Variables with Regression AnalysisWorking with Unlabeled Data - Clustering AnalysisImplementing Multilayer Artificial Neural NetworksParallelizing Neural Network Training with TensorFlowTensorFlow MechanicsClassifying Images with Deep Convolutional Neural NetworksModeling Sequential Data Using Recurrent Neural NetworksGANs for Synthesizing New DataRL for Decision Making in Complex Environments
Télécharger Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition de Sebastian Raschka Livre eBook France
GitHub - rasbt/python-machine-learning-book-3rd-edition ~ The "Python Machine Learning (3rd edition)" book code repository - rasbt/python-machine-learning-book-3rd-edition
scikit-learn: machine learning in Python — scikit-learn 0 ~ scikit-learn Machine Learning in Python Getting Started Release Highlights for 0.23 GitHub. Simple and efficient tools for predictive data analysis; Accessible to everybody, and reusable in various contexts ; Built on NumPy, SciPy, and matplotlib; Open source, commercially usable - BSD license; Classification. Identifying which category an object belongs to. Applications: Spam detection, image .
Hands on machine learning with scikit learn and tensorflow ~ Machine Learning Resources, Practice and Research. Contribute to yanshengjia/ml-road development by creating an account on GitHub.
Python Machine Learning: Machine Learning and Deep ~ Buy Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition 2nd Revised edition by Raschka, Sebastian, Mirjalili, Vahid (ISBN: 9781787125933) from Amazon's Book Store. Everyday low prices and free delivery on eligible orders.
Machine learning and deep learning guide — Databricks ~ Machine learning and deep learning guide Databricks is an environment that makes it easy to build, train, manage, and deploy machine learning and deep learning models at scale. Databricks integrates tightly with popular open-source libraries and with the MLflow machine learning platform API to support the end-to-end machine learning lifecycle from data preparation to deployment.
Machine learning education / TensorFlow ~ You will be introduced to ML with scikit-learn, guided through deep learning using TensorFlow 2.0, and then you will have the opportunity to practice what you learn with beginner tutorials. For intermediate level & experts Theoretical and advanced machine learning with TensorFlow Once you understand the basics of machine learning, take your abilities to the next level by diving into .
Machine Learning with Python Tutorial - Tutorialspoint ~ Machine Learning (ML) is basically that field of computer science with the help of which computer systems can provide sense to data in much the same way as human beings do. In simple words, ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method. The key focus of ML is to allow computer systems to learn from experience without being .
Initiation au Machine Learning avec Python - La théorie ~ Dans ce tutoriel en 2 parties nous vous proposons de découvrir les bases de l'apprentissage automatique et de vous y initier avec le langage Python. Cette première partie se veut non technique et présente les concepts du Machine Learning, les différents types d'apprentissage et leurs principaux algorithmes. Il situe enfin Python dans cet univers en présentant les nombreuses librairies à .
GitHub - ageron/handson-ml2: A series of Jupyter notebooks ~ Machine Learning Notebooks. This project aims at teaching you the fundamentals of Machine Learning in python. It contains the example code and solutions to the exercises in the second edition of my O'Reilly book Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow:. Note: If you are looking for the first edition notebooks, check out ageron/handson-ml.
An introduction to machine learning with scikit-learn ~ scikit-learn: machine learning in Python. classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data.An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories.
Manning / Deep Learning with Python ~ Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples.
Descargar Python Machine Learning Machine Learning And ~ Descargar Python Machine Learning Machine Learning And Deep Learning With Python Scikit Learn And Tensorflow Step By Step Tutorial For Beginners Updated English Edition/ PDF Gratis español. PDF Libros electrónicos gratuitos en todos los formatos para Android Apple y Kindle. Descargar ebooks gratis para llevar y leer en cualquier lugar.
Deep Learning with Python, TensorFlow, and Keras tutorial ~ An updated deep learning introduction using Python, TensorFlow, and Keras.Text-tutorial and notes: https://pythonprogramming/introduction-deep-learning-p.
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Nilearn: Statistical Analysis for NeuroImaging in Python ~ Nilearn enables approachable and versatile analyses of brain volumes.It provides statistical and machine-learning tools, with instructive documentation & open community. It supports general linear model (GLM) based analysis and leverages the scikit-learn Python toolbox for multivariate statistics with applications such as predictive modelling, classification, decoding, or connectivity analysis.
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Les meilleurs livres Python ~ Les meilleurs livres Python. 32 livres et 34 critiques, dernière mise à jour le 20 décembre 2020 , note moyenne : 4.3 Python. Petite leçon de Python - Introduction pratique et orientée projet Python 3 - Les fondamentaux du langage PYTHON.
Python Machine Learning - Second Edition: Machine Learning ~ 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.
Hands-On Machine Learning with Scikit-Learn, Keras, and ~ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems Aurélien Géron Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning.
TensorFlow ~ TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.
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Amazon - Building Machine Learning Systems with Python ~ Building Machine Learning Systems with Python: Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow, 3rd Edition 36,41 € Disponible pour expédition d'ici 1 à 2 jours.
Deep Learning with Python: Chollet, François ~ Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher François Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing .
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