Download or read book Introduction au Deep Learning written by Eugène Charniak and published by . This book was released on 2021-01-13 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning en Action written by Josh Patterson and published by . This book was released on 2018 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Plongez au coeur du Deep Learning Ce livre a été écrit pour tous ceux qui souhaitent s'initier au Deep Learning (apprentissage profond). Il est la suite logique du titre "Le Machine learning avec Python" paru en février 2018. Le Deep Learning est une technologie nouvelle qui évolue très rapidement. Ce livre en présente les bases principales de cette technologie. Au coeur de celle-ci on trouve les réseaux de neurones profonds, permettant de modéliser tous types de données et les réseaux de convolution, capables de traiter des images. Et enfin, cette technologie de plus en plus utilisée dans les applications d'intelligence artificielle introduit le notion de Reinforcement Learning (apprentissage par renforcement) qui permet d'optimiser les prises de décision par exemple pour le fonctionnement d'un robot. Au programme : La génèse du Deep Learning Les résaux de neuronnes Les bases des réseaux de type Deep learning L'architecture réseau Créer un réseau type Adapter le réseau à des besoins propres Les architectures spécifiques La vectorisation Le Deep Learning et DL4J sur Spark Au coeur de l'intelligence artificielle RL4J et Reinforcement Learning.

Download or read book Machine Learning avec Scikit Learn written by Aurélien Géron and published by . This book was released on 2023-11-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep learning avec TensorFlow written by Aurélien Géron and published by . This book was released on 2017-06-07 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Le Deep Learning (apprentissage profond) est un ensemble de techniques avancées du Machine Learning qui reposent principalement sur les réseaux de neurones. Le Deep Learning estau coeur des avancées extraordinaires en intelligence artificielle que l'on a pu observer ces dernières années : reconnaissance de la voix ou des visages, voitures autonomes, traduction automatique, etc. 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 open source créé et utilisé 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 varier les programmes pour mettre en oeuvre ses connaissances.

Download or read book Machine Learning avec Scikit Learn written by Aurélien Géron and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning avec TensorFlow written by Aurélien Géron and published by Dunod. This book was released on 2017-11-22 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Deep Learning Fundamentals Theory and Applications written by Kaizhu Huang and published by Springer. This book was released on 2019-02-15 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this edited volume is to provide a comprehensive overview on the fundamentals of deep learning, introduce the widely-used learning architectures and algorithms, present its latest theoretical progress, discuss the most popular deep learning platforms and data sets, and describe how many deep learning methodologies have brought great breakthroughs in various applications of text, image, video, speech and audio processing. Deep learning (DL) has been widely considered as the next generation of machine learning methodology. DL attracts much attention and also achieves great success in pattern recognition, computer vision, data mining, and knowledge discovery due to its great capability in learning high-level abstract features from vast amount of data. This new book will not only attempt to provide a general roadmap or guidance to the current deep learning methodologies, but also present the challenges and envision new perspectives which may lead to further breakthroughs in this field. This book will serve as a useful reference for senior (undergraduate or graduate) students in computer science, statistics, electrical engineering, as well as others interested in studying or exploring the potential of exploiting deep learning algorithms. It will also be of special interest to researchers in the area of AI, pattern recognition, machine learning and related areas, alongside engineers interested in applying deep learning models in existing or new practical applications.

Download or read book Deep Learning written by Shriram K Vasudevan and published by CRC Press. This book was released on 2021-12-24 with total page 307 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains, requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks, and continuing through the architecture of various types of CNNs, RNNs, LSTM, and more till the end of the book, each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory, examples, case studies, and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful.

Download or read book Deep Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2019-04-17 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: Take a deep dive into deep learning Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it. In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types. Includes sample code Provides real-world examples within the approachable text Offers hands-on activities to make learning easier Shows you how to use Deep Learning more effectively with the right tools This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Download or read book Deep Learning written by Manel Martinez-Ramon and published by John Wiley & Sons. This book was released on 2024-09-10 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Download or read book Deep Learning written by Manel Martinez-Ramon and published by John Wiley & Sons. This book was released on 2024-07-08 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: An engaging and accessible introduction to deep learning perfect for students and professionals In Deep Learning: A Practical Introduction, a team of distinguished researchers delivers a book complete with coverage of the theoretical and practical elements of deep learning. The book includes extensive examples, end-of-chapter exercises, homework, exam material, and a GitHub repository containing code and data for all provided examples. Combining contemporary deep learning theory with state-of-the-art tools, the chapters are structured to maximize accessibility for both beginning and intermediate students. The authors have included coverage of TensorFlow, Keras, and Pytorch. Readers will also find: Thorough introductions to deep learning and deep learning tools Comprehensive explorations of convolutional neural networks, including discussions of their elements, operation, training, and architectures Practical discussions of recurrent neural networks and non-supervised approaches to deep learning Fulsome treatments of generative adversarial networks as well as deep Bayesian neural networks Perfect for undergraduate and graduate students studying computer vision, computer science, artificial intelligence, and neural networks, Deep Learning: A Practical Introduction will also benefit practitioners and researchers in the fields of deep learning and machine learning in general.

Download or read book Deep Learning Illustrated written by Jon Krohn and published by Addison-Wesley Professional. This book was released on 2019-08-05 with total page 725 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come." – Tim Urban, author of Wait But Why Fully Practical, Insightful Guide to Modern Deep Learning Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn. World-class instructor and practitioner Jon Krohn–with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens–presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered. You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms. Discover what makes deep learning systems unique, and the implications for practitioners Explore new tools that make deep learning models easier to build, use, and improve Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

Download or read book Learning Deep Textwork written by René-Marcel Kruse and published by Universitätsverlag Göttingen. This book was released on 2021 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence is considered to be one of the most decisive topics in the 21st century. Deep learning algorithms, which are the basis of many artificial intelligence applications, are of central interest for researchers but also for students that strive to build up academic knowledge and practical competencies in this field. The Deep Learning Seminar at the University of Göttingen follows the central notion of the Humboldtian model of higher education and offers graduate students of applied statistics the opportunity to conduct their own research. The quality of the results motivated us to publish the most promising seminar papers in this volume. For the selected papers a review process was conducted by the lecturers. The presented contributions focus on applications of deep learning algorithms for text data. Natural language processing methods are for example applied to analyse data from Twitter, Telegram and Newspapers. The research applications allow the reader to gain deep insights into some of the latest developments in the field of artificial intelligence and natural language processing from the perspective of students of whom many will take part in shaping the future research in this field.

Download or read book Deep Learning Theory Architectures and Applications in Speech Image and Language Processing written by Gyanendra Verma and published by Bentham Science Publishers. This book was released on 2023-08-21 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. This book is divided into three parts. The first part explains the basic operating understanding, history, evolution, and challenges associated with deep learning. The basic concepts of mathematics and the hardware requirements for deep learning implementation, and some of its popular frameworks for medical applications are also covered. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Download or read book Deep Learning Applications written by M. Arif Wani and published by Springer Nature. This book was released on 2020-02-28 with total page 184 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a compilation of selected papers from the 17th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2018), focusing on use of deep learning technology in application like game playing, medical applications, video analytics, regression/classification, object detection/recognition and robotic control in industrial environments. It highlights novel ways of using deep neural networks to solve real-world problems, and also offers insights into deep learning architectures and algorithms, making it an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.

Download or read book Organisations hautement durables Gouvernance risques et crit res d apprentissage written by MERAD Myriam and published by Lavoisier. This book was released on 2013-12-04 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Le « développement durable » est un projet de société séduisant qui reste relativement théorique en raison du déficit de retours d’expériences opérationnelles et méthodologiques. Organisations hautement durables a pour objectif de rendre compte, de comprendre et d’accompagner la mise en pratique du développement durable au sein d’entreprises et d’organismes publics. À ce titre, cet ouvrage : – définit le concept d’organisation hautement durable en s’appuyant sur les notions de risques organisationnels et sur ce qui est à préserver : « le capital critique » ; – identifie les stades d’apprentissage organisationnel en vue d’atteindre les caractéristiques d’une organisation hautement durable et responsable ; – souligne l’intérêt des indicateurs et pointe leurs dérives d’usage ; – propose des méthodes d’aide multicritère à la décision, notamment pour la mise en place d’une stratégie et d’un plan d’actions développement durable ; – aborde les difficultés opérationnelles du changement organisationnel et discute des moyens et des leviers d’actions pour les dépasser ; – soulève l’importance du rôle de la gouvernance dans ce cadre et propose des fondements et des critères de son évaluation. Illustré de nombreux exemples et d’expériences de terrain, cet ouvrage repose sur un partage des pratiques et sur une approche scientifique. Il propose une ingénierie avec de véritables outils politiques, organisationnels et techniques pour aider les dirigeants d’entreprises et d’organismes publics à opérer un changement vers un nouveau schéma d’organisation axé sur la gestion des problématiques environnementales et sociétales. Organisations hautement durables s’adresse aux décideurs, aux analystes, aux ingénieurs et aux consultants qui ont à répondre à une demande particulière en matière de développement durable et de responsabilité sociétale.