Livres sur le deep learning 🔝

"Apprendre Python: Un Cours Accéléré sur la Programmation Python et Comment Commencer à l’utiliser pour Coder. Apprenez les Bases de Machine Learning et de l'analyse de Données" de Parker, Damon est un livre qui propose un cours accéléré sur la programmation Python, en mettant l'accent sur la façon de commencer à coder avec Python. Le livre se concentre également sur l'apprentissage des bases du Machine Learning et de l'analyse de données en utilisant Python. Il couvre les concepts de base de la programmation Python, tels que les variables, les boucles, les fonctions, les listes et les dictionnaires. En plus de cela, le livre explore comment utiliser Python pour l'analyse de données en utilisant des bibliothèques telles que NumPy et Pandas. Il plonge également dans les bases du Machine Learning en utilisant des packages tels que Scikit-Learn. Ce livre est idéal pour les débutants en programmation Python qui souhaitent apprendre les bases du codage ainsi que les principes fondamentaux de l'analyse de données et du Machine Learning. Il fournit une introduction pratique et étape par étape à Python et offre des exemples de code clairs et concis pour illustrer chaque concept. Que vous soyez un étudiant, un ingénieur ou un professionnel cherchant à élargir vos compétences, "Apprendre Python: Un Cours Accéléré sur la Programmation Python et Comment Commencer à l’utiliser pour Coder. Apprenez les Bases de Machine Learning et de l'analyse de Données" vous aidera à maîtriser les bases de Python et à commencer à utiliser Python pour coder et analyser des données.

"Crainging The Machine Learning Interview" is a book written by Nitin Suri. It is a comprehensive guide for individuals preparing for machine learning interviews. The book covers various topics such as data preprocessing, feature selection, model training, and evaluation. It also provides sample interview questions and solutions for better understanding. The book aims to assist readers in understanding and mastering the key concepts and techniques required for machine learning interviews.

Deep Learning: Unlocking Our Emotions by Digital, Cervantes is a book that explores the intersection between deep learning and human emotions. The authors delve into the potential of deep learning algorithms to understand and interpret human emotions, and how this can impact various fields such as psychology, marketing, and user experience design. The book begins with an introduction to deep learning, explaining the basic concepts and algorithms used in this field. It then moves on to discuss the importance of emotions in human communication and behavior, highlighting the challenges of accurately understanding and interpreting emotions. The authors then delve into the various applications of deep learning in emotion recognition and analysis. They discuss how deep learning algorithms can be trained on large datasets of facial expressions, vocal tones, and physiological signals to accurately recognize and classify different emotions. They also explore the potential of deep learning in generating emotionally intelligent responses in virtual agents and chatbots. Furthermore, the book explores the ethical considerations and potential risks associated with using deep learning in understanding emotions. It raises important questions about privacy, bias, and the potential manipulation of emotions through AI algorithms. Throughout the book, the authors provide examples and case studies to illustrate the practical applications of deep learning in understanding emotions. They also discuss the current state of research and highlight the future directions and challenges in this field. Overall, Deep Learning: Unlocking Our Emotions provides an in-depth exploration of the potential of deep learning algorithms in understanding and interpreting human emotions. It is a valuable resource for researchers, professionals, and anyone interested in the intersection of artificial intelligence and human emotions.

Multimodality Imaging, Volume 1: Deep learning applications is a comprehensive book that explores the use of deep learning techniques in medical imaging. Written by Jasjit S. Suri, a renowned researcher in the field of medical image analysis, this book provides a detailed overview of how deep learning algorithms can be applied to multimodality imaging data. The book covers a wide range of topics, including the basics of deep learning, image segmentation, image classification, and image registration. It also delves into advanced applications of deep learning in multimodality imaging, such as image fusion, image reconstruction, and image analysis. With contributions from leading experts in the field, Multimodality Imaging, Volume 1: Deep learning applications offers valuable insights into the latest developments in medical imaging technology. This book is a must-read for researchers, clinicians, and students interested in the intersection of deep learning and medical imaging.