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Hands-On Image Processing with Python

Telecharger pdf Hands-On Image Processing with Python

Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. Key Features Practical coverage of eve ...

  • L'auteur : Sandipan Dey
  • Editeur: Packt Publishing Ltd
  • ISBN: 178934185X
  • Genre: Computers
  • Nombre de pages: 492
  • Langue: French/English
  • Vues: 1013
  • Telecharger: 1013
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Détails: Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks. Key Features Practical coverage of every image processing task with popular Python libraries Includes topics such as pseudo-coloring, noise smoothing, computing image descriptors Covers popular machine learning and deep learning techniques for complex image processing tasks Book Description Image processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python. The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing. By the end of this book, we will have learned to implement various algorithms for efficient image processing. What you will learn Perform basic data pre-processing tasks such as image denoising and spatial filtering in Python Implement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in Python Do morphological image processing and segment images with different algorithms Learn techniques to extract features from images and match images Write Python code to implement supervised / unsupervised machine learning algorithms for image processing Use deep learning models for image classification, segmentation, object detection and style transfer Who this book is for This book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.


Hands-On Data Analysis with Pandas

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Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key Features Perform efficient da ...

  • L'auteur : Stefanie Molin
  • Editeur: Packt Publishing Ltd
  • ISBN: 1789612802
  • Genre: Computers
  • Nombre de pages: 716
  • Langue: French/English
  • Vues: 930
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Détails: Get to grips with pandas—a versatile and high-performance Python library for data manipulation, analysis, and discovery Key Features Perform efficient data analysis and manipulation tasks using pandas Apply pandas to different real-world domains using step-by-step demonstrations Get accustomed to using pandas as an effective data exploration tool Book Description Data analysis has become a necessary skill in a variety of positions where knowing how to work with data and extract insights can generate significant value. Hands-On Data Analysis with Pandas will show you how to analyze your data, get started with machine learning, and work effectively with Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the powerful pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification, using scikit-learn, to make predictions based on past data. By the end of this book, you will be equipped with the skills you need to use pandas to ensure the veracity of your data, visualize it for effective decision-making, and reliably reproduce analyses across multiple datasets. What you will learn Understand how data analysts and scientists gather and analyze data Perform data analysis and data wrangling in Python Combine, group, and aggregate data from multiple sources Create data visualizations with pandas, matplotlib, and seaborn Apply machine learning (ML) algorithms to identify patterns and make predictions Use Python data science libraries to analyze real-world datasets Use pandas to solve common data representation and analysis problems Build Python scripts, modules, and packages for reusable analysis code Who this book is for This book is for data analysts, data science beginners, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. You will also find this book useful if you are a data scientist who is looking to implement pandas in machine learning. Working knowledge of Python programming language will be beneficial.


Hands-On Explainable AI (XAI) with Python

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In today’s era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This boo ...

  • L'auteur : Denis Rothman
  • Editeur: Packt Publishing Ltd
  • ISBN: 1800202768
  • Genre: Computers
  • Nombre de pages: 454
  • Langue: French/English
  • Vues: 1185
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Détails: In today’s era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI’s pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate AI.


Machine Learning avec Scikit-Learn

Telecharger pdf Machine Learning avec Scikit-Learn

L’apprentissage automatique (Machine Learning) est aujourd’hui en pleine explosion. Mais de quoi s’agit-il exactement, et comment pouvez-vous le mett ...

  • L'auteur : Aurélien Géron
  • Editeur: Dunod
  • ISBN: 2100797824
  • Genre: Computers
  • Nombre de pages: 256
  • Langue: French/English
  • Vues: 681
  • Telecharger: 681
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Détails: L’apprentissage automatique (Machine Learning) est aujourd’hui en pleine explosion. Mais de quoi s’agit-il exactement, et comment pouvez-vous le mettre en oeuvre dans vos propres projets ? L’objectif de cet ouvrage est de vous expliquer les concepts fondamentaux du Machine Learning et de vous apprendre à maîtriser les outils qui vous permettront de créer vous-même des systèmes capables d’apprentissage automatique. Vous apprendrez ainsi à utiliser Scikit-Learn, un outil open source très simple et néanmoins très puissant que vous pourrez mettre en oeuvre dans vos systèmes en production. • Apprendre les bases du Machine Learning en suivant pas à pas toutes les étapes d’un projet utilisant Scikit-Learn et pandas. • Ouvrir les boîtes noires pour comprendre comment fonctionnent les algorithmes. • Explorer plusieurs modèles d’entraînement, notamment les machines à vecteur de support (SVM). • Comprendre le modèle des arbres de décision et celui des forêts aléatoires, et exploiter la puissance des méthodes ensemblistes. • Exploiter des techniques d’apprentissage non supervisées telles que la réduction de dimensionnalité, la classification et la détection d’anomalies.


Hands-On Transfer Learning with Python

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Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build dee ...

  • L'auteur : Dipanjan Sarkar,Raghav Bali,Tamoghna Ghosh
  • Editeur: Packt Publishing Ltd
  • ISBN: 1788839056
  • Genre: Computers
  • Nombre de pages: 438
  • Langue: French/English
  • Vues: 1220
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Détails: Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.


Hands-On Genetic Algorithms with Python

Telecharger pdf Hands-On Genetic Algorithms with Python

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python ...

  • L'auteur : Eyal Wirsansky
  • Editeur: Packt Publishing Ltd
  • ISBN: 1838559183
  • Genre: Computers
  • Nombre de pages: 346
  • Langue: French/English
  • Vues: 1194
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Détails: Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPy Key Features Explore the ins and outs of genetic algorithms with this fast-paced guide Implement tasks such as feature selection, search optimization, and cluster analysis using Python Solve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applications Book Description Genetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.


Python for Finance

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A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python ...

  • L'auteur : Yuxing Yan
  • Editeur: Packt Publishing Ltd
  • ISBN: 1783284382
  • Genre: Computers
  • Nombre de pages: 408
  • Langue: French/English
  • Vues: 418
  • Telecharger: 418
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Détails: A hands-on guide with easy-to-follow examples to help you learn about option theory, quantitative finance, financial modeling, and time series using Python. Python for Finance is perfect for graduate students, practitioners, and application developers who wish to learn how to utilize Python to handle their financial needs. Basic knowledge of Python will be helpful but knowledge of programming is necessary.


Neural Network Projects with Python

Telecharger pdf Neural Network Projects with Python

Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neu ...

  • L'auteur : James Loy
  • Editeur: Packt Publishing Ltd
  • ISBN: 1789133319
  • Genre: Computers
  • Nombre de pages: 308
  • Langue: French/English
  • Vues: 1759
  • Telecharger: 1759
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Détails: Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in Python Key Features Discover neural network architectures (like CNN and LSTM) that are driving recent advancements in AI Build expert neural networks in Python using popular libraries such as Keras Includes projects such as object detection, face identification, sentiment analysis, and more Book Description 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. What you will learn Learn various neural network architectures and its advancements in AI Master deep learning in Python by building and training neural network Master neural networks for regression and classification Discover convolutional neural networks for image recognition Learn sentiment analysis on textual data using Long Short-Term Memory Build and train a highly accurate facial recognition security system Who this book is for This book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.


Deep Learning avec TensorFlow

Telecharger pdf Deep Learning avec TensorFlow

Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au Deep Learning (apprentissage profond) est la traduction de la deuxième partie du best-selle ...

  • L'auteur : Aurélien Géron
  • Editeur: Dunod
  • ISBN: 210076747X
  • Genre: Computers
  • Nombre de pages: 256
  • Langue: French/English
  • Vues: 338
  • Telecharger: 338
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Détails: Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au Deep Learning (apprentissage profond) est la traduction de la deuxième partie du best-seller américain Hands-On Machine Learning with Scikit-Learn & TensorFloW. Le Deep Learning est récent et il évolue vite. Ce livre en présente les principales techniques : les réseaux de neurones profonds, capables de modéliser toutes sortes de données, les réseaux de convolution, capables de classifier des images, les segmenter et découvrir les objets ou personnes qui s'y trouvent, les réseaux récurrents, capables de gérer des séquences telles que des phrases, des séries temporelles, ou encore des vidéos, les Autoencoders qui peuvent découvrir toutes sortes de structures dans des données, de façon non supervisée, et enfin le Reinforcement Learning (apprentissage par renforcement) qui permet de découvrir automatiquement les meilleures actions pour effectuer une tâche (par exemple un robot qui apprend à marcher). Ce livre présente TensorFlow, le framework de Deep Learning créé par Google. Il est accompagné de Jupyter notebooks (disponibles sur github) qui contiennent tous les exemples de code du livre, afin que le lecteur puisse facilement tester et faire tourner les programmes. Il complète un premier livre intitulé Machine Learning avec Scikit-Learn.


Machine learning avec Python ANNULE

Telecharger pdf Machine learning avec Python ANNULE

Entrez de plain-pied dans le monde fascinant la data science Vous aussi participez à la révolution qui ramène l'intelligence artificielle au coeur de no ...

  • L'auteur : Andreas C.MUELLER,Sarah GUIDO
  • Editeur: First Interactive
  • ISBN: 2412037019
  • Genre: Computers
  • Nombre de pages: 282
  • Langue: French/English
  • Vues: 1905
  • Telecharger: 1905
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Détails: Entrez de plain-pied dans le monde fascinant la data science Vous aussi participez à la révolution qui ramène l'intelligence artificielle au coeur de notre société, grace aux data scientists. La data science consiste à traduire des problèmes de toute autre nature, en problèmes de modélisation quantitative, résolus par des algorithmes de traitement. Ce livre se présente comme une référence pour tous les développeurs, statisticiens ou chefs de projets ayant à résoudre des problèmes liés à la data science. Au programme : Pourquoi utiliser le machine learning Les différentes versions de Python L'apprentissage non supervisé et le préprocessing Représenter les données Processus de validation Algorithmes, chaînes et pipeline Travailler avec des données de type texte Du prototype à la production


Learning Data Mining with Python

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Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Netw ...

  • L'auteur : Robert Layton
  • Editeur: Packt Publishing Ltd
  • ISBN: 178712956X
  • Genre: Computers
  • Nombre de pages: 358
  • Langue: French/English
  • Vues: 1979
  • Telecharger: 1979
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Détails: Harness the power of Python to develop data mining applications, analyze data, delve into machine learning, explore object detection using Deep Neural Networks, and create insightful predictive models. About This Book Use a wide variety of Python libraries for practical data mining purposes. Learn how to find, manipulate, analyze, and visualize data using Python. Step-by-step instructions on data mining techniques with Python that have real-world applications. Who This Book Is For If you are a Python programmer who wants to get started with data mining, then this book is for you. If you are a data analyst who wants to leverage the power of Python to perform data mining efficiently, this book will also help you. No previous experience with data mining is expected. What You Will Learn Apply data mining concepts to real-world problems Predict the outcome of sports matches based on past results Determine the author of a document based on their writing style Use APIs to download datasets from social media and other online services Find and extract good features from difficult datasets Create models that solve real-world problems Design and develop data mining applications using a variety of datasets Perform object detection in images using Deep Neural Networks Find meaningful insights from your data through intuitive visualizations Compute on big data, including real-time data from the internet In Detail This book teaches you to design and develop data mining applications using a variety of datasets, starting with basic classification and affinity analysis. This book covers a large number of libraries available in Python, including the Jupyter Notebook, pandas, scikit-learn, and NLTK. You will gain hands on experience with complex data types including text, images, and graphs. You will also discover object detection using Deep Neural Networks, which is one of the big, difficult areas of machine learning right now. With restructured examples and code samples updated for the latest edition of Python, each chapter of this book introduces you to new algorithms and techniques. By the end of the book, you will have great insights into using Python for data mining and understanding of the algorithms as well as implementations. Style and approach This book will be your comprehensive guide to learning the various data mining techniques and implementing them in Python. A variety of real-world datasets is used to explain data mining techniques in a very crisp and easy to understand manner.


L'épouse bafouée (Harlequin Azur)

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L'épouse bafouée, Miranda Lee Déjà durement éprouvée par une fausse couche, Megan surprend à l'hôpital une conversation entre les deux meilleurs am ...

  • L'auteur : Miranda Lee
  • Editeur: Harlequin
  • ISBN: 2280272636
  • Genre: Fiction
  • Nombre de pages: 160
  • Langue: French/English
  • Vues: 1489
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Détails: L'épouse bafouée, Miranda Lee Déjà durement éprouvée par une fausse couche, Megan surprend à l'hôpital une conversation entre les deux meilleurs amis de son mari. Une conversation qui la laisse en proie au désespoir le plus profond : son mari, apprend-elle, ne l'a jamais aimée et ne cherchait, en l'épousant, qu'à avoir un héritier... En pensant à ce que va devenir son mariage, Megan sent la panique la submerger. Peut-elle continuer à vivre avec James comme si de rien n'était, alors qu'il n'éprouve rien pour elle ?


Programmez un Raspberry Pi

Telecharger pdf Programmez un Raspberry Pi

Le but de ce court ouvrage est de vous montrer comment créer des programmes et des jeux amusants sur votre Raspberry Pi en utilisant le langage Python (le ...

  • L'auteur : Simon Monk
  • Editeur: Dunod
  • ISBN: 2100708678
  • Genre: Technology & Engineering
  • Nombre de pages: 192
  • Langue: French/English
  • Vues: 453
  • Telecharger: 453
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Détails: Le but de ce court ouvrage est de vous montrer comment créer des programmes et des jeux amusants sur votre Raspberry Pi en utilisant le langage Python (le "Pi" de Raspberry Pi...). L'auteur explique les bases du développement sur cet ordinateur un peu spécial, à la fois minuscule et puissant. Il vous fournit des exemples "qui marchent" et des scripts prêts à l'emploi. Il vous montre comment installer matériels et logiciels, comment écrire et débugger ses premières applis, et contrôler des périphériques électroniques. L'ouvrage se termine par deux projets concrets : une horloge à LED et un logiciel de pilotage de robot.


Data Science Projects with Python

Telecharger pdf Data Science Projects with Python

Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify th ...

  • L'auteur : Stephen Klosterman
  • Editeur: Packt Publishing Ltd
  • ISBN: 183855260X
  • Genre: Computers
  • Nombre de pages: 374
  • Langue: French/English
  • Vues: 1299
  • Telecharger: 1299
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Détails: Gain hands-on experience with industry-standard data analysis and machine learning tools in Python Key Features Learn techniques to use data to identify the exact problem to be solved Visualize data using different graphs Identify how to select an appropriate algorithm for data extraction Book Description Data Science Projects with Python is designed to give you practical guidance on industry-standard data analysis and machine learning tools in Python, with the help of realistic data. The book will help you understand how you can use pandas and Matplotlib to critically examine a dataset with summary statistics and graphs, and extract the insights you seek to derive. You will continue to build on your knowledge as you learn how to prepare data and feed it to machine learning algorithms, such as regularized logistic regression and random forest, using the scikit-learn package. You’ll discover how to tune the algorithms to provide the best predictions on new and, unseen data. As you delve into later chapters, you’ll be able to understand the working and output of these algorithms and gain insight into not only the predictive capabilities of the models but also their reasons for making these predictions. By the end of this book, you will have the skills you need to confidently use various machine learning algorithms to perform detailed data analysis and extract meaningful insights from unstructured data. What you will learn Install the required packages to set up a data science coding environment Load data into a Jupyter Notebook running Python Use Matplotlib to create data visualizations Fit a model using scikit-learn Use lasso and ridge regression to reduce overfitting Fit and tune a random forest model and compare performance with logistic regression Create visuals using the output of the Jupyter Notebook Who this book is for If you are a data analyst, data scientist, or a business analyst who wants to get started with using Python and machine learning techniques to analyze data and predict outcomes, this book is for you. Basic knowledge of computer programming and data analytics is a must. Familiarity with mathematical concepts such as algebra and basic statistics will be useful.


Deep Learning avec Keras et TensorFlow

Telecharger pdf Deep Learning avec Keras et TensorFlow

Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au deep learning (apprentissage profond), est la traduction de la deuxième partie du best-sell ...

  • L'auteur : Aurélien Géron
  • Editeur: Dunod
  • ISBN: 2100805029
  • Genre: Computers
  • Nombre de pages: 360
  • Langue: French/English
  • Vues: 1535
  • Telecharger: 1535
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Détails: Cet ouvrage, conçu pour tous ceux qui souhaitent s'initier au deep learning (apprentissage profond), est la traduction de la deuxième partie du best-seller américain Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow (2e édition). Le deep learning est récent et il évolue vite. Ce livre en présente les principales techniques : les réseaux de neurones profonds, capables de modéliser toutes sortes de données, les réseaux de convolution, capables de classifier des images, les segmenter et découvrir les objets ou personnes qui s'y trouvent, les réseaux récurrents, capables de gérer des séquences telles que des phrases, des séries temporelles, ou encore des vidéos, les autoencodeurs qui peuvent découvrir toutes sortes de structures dans des données, de façon non supervisée, et enfin le reinforcement learning (apprentissage par renforcement) qui permet de découvrir automatiquement les meilleures actions pour effectuer une tâche (par exemple un robot qui apprend à marcher). Ce livre présente TensorFlow, le framework de deep learning créé par Google. Il est accompagné de notebooks Jupyter qui contiennent tous les exemples de code du livre, afin que le lecteur puisse facilement tester et faire tourner les programmes. Il complète un premier livre du même auteur intitulé Machine Learning avec Scikit-Learn.


Learning Python

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Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scrip ...

  • L'auteur : Mark Lutz,David Ascher
  • Editeur: \"O\'Reilly Media, Inc.\"
  • ISBN: 9780596551933
  • Genre: Computers
  • Nombre de pages: 624
  • Langue: French/English
  • Vues: 1672
  • Telecharger: 1672
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Détails: Portable, powerful, and a breeze to use, Python is the popular open source object-oriented programming language used for both standalone programs and scripting applications. Python is considered easy to learn, but there's no quicker way to mastery of the language than learning from an expert teacher. This edition of Learning Python puts you in the hands of two expert teachers, Mark Lutz and David Ascher, whose friendly, well-structured prose has guided many a programmer to proficiency with the language. Learning Python, Second Edition, offers programmers a comprehensive learning tool for Python and object-oriented programming. Thoroughly updated for the numerous language and class presentation changes that have taken place since the release of the first edition in 1999, this guide introduces the basic elements of the latest release of Python 2.3 and covers new features, such as list comprehensions, nested scopes, and iterators/generators. Beyond language features, this edition of Learning Python also includes new context for less-experienced programmers, including fresh overviews of object-oriented programming and dynamic typing, new discussions of program launch and configuration options, new coverage of documentation sources, and more. There are also new use cases throughout to make the application of language features more concrete. The first part of Learning Python gives programmers all the information they'll need to understand and construct programs in the Python language, including types, operators, statements, classes, functions, modules and exceptions. The authors then present more advanced material, showing how Python performs common tasks by offering real applications and the libraries available for those applications. Each chapter ends with a series of exercises that will test your Python skills and measure your understanding. Learning Python, Second Edition is a self-paced book that allows readers to focus on the core Python language in depth. As you work through the book, you'll gain a deep and complete understanding of the Python language that will help you to understand the larger application-level examples that you'll encounter on your own. If you're interested in learning Python--and want to do so quickly and efficiently--then Learning Python, Second Edition is your best choice.


Programmer en Python - Apprendre la programmation de façon claire, concise et efficace - collection O'Reilly

Telecharger pdf Programmer en Python - Apprendre la programmation de façon claire, concise et efficace - collection O'Reilly

Un livre unique pour aller plus loin dans la mise en oeuvre de vos développements Python La simplicité du langage Python permet d'être productif très r ...

  • L'auteur : Luciano RAMALHO
  • Editeur: First Interactive
  • ISBN: 2412047162
  • Genre: Computers
  • Nombre de pages: 682
  • Langue: French/English
  • Vues: 1413
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Détails: Un livre unique pour aller plus loin dans la mise en oeuvre de vos développements Python La simplicité du langage Python permet d'être productif très rapidement mais cela ne signifie pas que vous utilisez tout le potentiel du langage. Avec ce livre en mains, vous allez apprendre à coder en Python de manière beaucoup plus efficace en découvrant des caractéristiques du langage ignorées de beaucoup. L'auteur, Luciano Ramalho vous guide dans un voyage intitiatique à travers les bibliothèques et les éléments du langage les plus intéressants. Vos nouvelle connaissances vous permettront de réduire vos lignes de codes et de rendre vos programmes plus rapides et globalement plus efficaces. Au programme : Le modèle de données de Python Les structures de données Les fonctions et les objets Classes, références, interfaces et opérateurs Le contrôle du flux La métaprogrammation Collection O'Reilly


Python pour les kids

Telecharger pdf Python pour les kids

La programmation accessible à tous ! Python est un langage de programmation puissant, expressif, facile à apprendre et amusant. Il est compatible avec Ma ...

  • L'auteur : Jason R. Briggs
  • Editeur: Editions Eyrolles
  • ISBN: 2212319282
  • Genre: Computers
  • Nombre de pages: 332
  • Langue: French/English
  • Vues: 1183
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Détails: La programmation accessible à tous ! Python est un langage de programmation puissant, expressif, facile à apprendre et amusant. Il est compatible avec Mac, Windows et Linux. Python pour les kids donne vie à Python et t'emmène, ainsi que tes parents, dans l'univers de la programmation. Avec des trésors de patience, Jason R. Briggs te guidera parmi les bases, à mesure que tu t'essaieras à des exemples de programmes uniques et parfois hilarants, qui mettent en lumière des monstres voraces, des sorciers, des agents secrets, des corbeaux voleurs et d'autres curiosités du genre. Les définitions des termes utilisés, le code colorisé et expliqué en détail, ainsi que des illustrations en couleurs agrémentent l'apprentissage et le rendent plus aisé. Les fins de chapitres proposent des puzzles de programmation pour t'entraîner. À la fin du livre, tu auras programmé deux jeux complets : un clone du fameux jeu de pong (balle bondissante et raquette) et "M. Filiforme court vers la sortie", un jeu de plates- formes avec des sauts, des animations et bien plus. A partir de 10 ans Tout au long de cette aventure, tu apprendras à : te servir des structures de données fondamentales comme les listes, les tuples et les dictionnaires ; organiser et réutiliser ton code à l'aide de fonctions, de classes et de modules ; utiliser les structures de contrôle comme les boucles et les instructions conditionnelles ; dessiner des formes et des motifs à l'aide du module de la tortue de Python ; créer des jeux, des animations et d'autres merveilles avec tkinter. Pourquoi les adultes seraient-ils seuls à s'amuser ? Python pour les kids est ton ticket d'entrée dans le monde merveilleux de la programmation.


Foundations of Deep Reinforcement Learning

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The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning ...

  • L'auteur : Laura Graesser,Wah Loon Keng
  • Editeur: Addison-Wesley Professional
  • ISBN: 0135172489
  • Genre: Computers
  • Nombre de pages: 416
  • Langue: French/English
  • Vues: 341
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Détails: The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and Practice Deep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer games—such as Go, Atari games, and DotA 2—to robotics. Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work. This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python. Understand each key aspect of a deep RL problem Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER) Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO) Understand how algorithms can be parallelized synchronously and asynchronously Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work Explore algorithm benchmark results with tuned hyperparameters Understand how deep RL environments are designed Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.


Machine Learning

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A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several ...

  • L'auteur : Stephen Marsland
  • Editeur: CRC Press
  • ISBN: 1466583339
  • Genre: Business & Economics
  • Nombre de pages: 457
  • Langue: French/English
  • Vues: 750
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Détails: A Proven, Hands-On Approach for Students without a Strong Statistical FoundationSince the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students