Nouvelles acquisitions GENES
Nouveautés reçues dans toutes les bibliothèques
trié(s) par Alpha (Titre croissant(e)) Ajouter le résultat dans votre panier Affiner la rechercheCracking the Data Science Interview / Leondra R. Gonzalez / Birmingham (GBR) ; Olton (GBR) : Packt Publishing (2024)
Titre : Cracking the Data Science Interview : Unlock insider tips from industry experts to master the data science field Type de document : texte imprimé Auteurs : Leondra R. Gonzalez ; Aaren Stubberfield Editeur : Birmingham (GBR) ; Olton (GBR) : Packt Publishing Année de publication : 2024 Importance : xx-384 p. ISBN/ISSN/EAN : 978-1-80512-050-6 Langues : Anglais (eng) Langues originales : Anglais (eng) Descripteurs : Données massives , Mathématiques financières , Recrutement Tags : Orientation professionnelle Entretiens de recrutement Index. décimale : 786 Mathématiques financières Résumé : The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job. [Description de l'éditeur) En ligne : https://www.packtpub.com/en-fr/product/cracking-the-data-science-interview-97818 [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177618 Cracking the Data Science Interview : Unlock insider tips from industry experts to master the data science field [texte imprimé] / Leondra R. Gonzalez ; Aaren Stubberfield . - Birmingham (GBR) ; Olton (GBR) : Packt Publishing, 2024 . - xx-384 p.
ISBN : 978-1-80512-050-6
Langues : Anglais (eng) Langues originales : Anglais (eng)
Descripteurs : Données massives , Mathématiques financières , Recrutement Tags : Orientation professionnelle Entretiens de recrutement Index. décimale : 786 Mathématiques financières Résumé : The data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company. Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you’ll find tips on job hunting, resume writing, and creating a top-notch portfolio. You’ll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview. By the end of this interview guide, you’ll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job. [Description de l'éditeur) En ligne : https://www.packtpub.com/en-fr/product/cracking-the-data-science-interview-97818 [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177618 Réservation
Réserver ce document
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité E020851 786 GON Ouvrage ENSAE 7. Économie appliquée - Politiques économiques - Finance Disponible
Titre : Deep Learning for Time Series : Cookbook Type de document : texte imprimé Auteurs : Vitor Cerqueira ; Luis Roque, Auteur Editeur : Birmingham (GBR) ; Olton (GBR) : Packt Publishing Année de publication : 2024 Importance : xx-254 p. ISBN/ISSN/EAN : 978-1-80512-923-3 Langues : Anglais (eng) Langues originales : Anglais (eng) Descripteurs : Apprentissage automatique , Série temporelle Tags : Deep learning Index. décimale : 24 Séries temporelles - Prévision Résumé : Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem( Description de l'éditeur)
En ligne : https://www.packtpub.com/en-us/product/deep-learning-for-time-series-cookbook-97 [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177623 Deep Learning for Time Series : Cookbook [texte imprimé] / Vitor Cerqueira ; Luis Roque, Auteur . - Birmingham (GBR) ; Olton (GBR) : Packt Publishing, 2024 . - xx-254 p.
ISBN : 978-1-80512-923-3
Langues : Anglais (eng) Langues originales : Anglais (eng)
Descripteurs : Apprentissage automatique , Série temporelle Tags : Deep learning Index. décimale : 24 Séries temporelles - Prévision Résumé : Most organizations exhibit a time-dependent structure in their processes, including fields such as finance. By leveraging time series analysis and forecasting, these organizations can make informed decisions and optimize their performance. Accurate forecasts help reduce uncertainty and enable better planning of operations. Unlike traditional approaches to forecasting, deep learning can process large amounts of data and help derive complex patterns. Despite its increasing relevance, getting the most out of deep learning requires significant technical expertise. This book guides you through applying deep learning to time series data with the help of easy-to-follow code recipes. You’ll cover time series problems, such as forecasting, anomaly detection, and classification. This deep learning book will also show you how to solve these problems using different deep neural network architectures, including convolutional neural networks (CNNs) or transformers. As you progress, you’ll use PyTorch, a popular deep learning framework based on Python to build production-ready prediction solutions. By the end of this book, you'll have learned how to solve different time series tasks with deep learning using the PyTorch ecosystem( Description de l'éditeur)
En ligne : https://www.packtpub.com/en-us/product/deep-learning-for-time-series-cookbook-97 [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177623 Réservation
Réserver ce document
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité E020850 24 CER Ouvrage ENSAE 2. Statistique Disponible
Titre : Deep Learning Foundations Type de document : texte imprimé Auteurs : Jo Taeho Editeur : Cham : Springer Nature Switzerland Année de publication : 2023 Importance : xx-426 p. ISBN/ISSN/EAN : 978-3-031-32881-7 Langues : Anglais (eng) Langues originales : Anglais (eng) Descripteurs : Apprentissage automatique , Réseaux de neurones Index. décimale : 216 Statistique computationnelle Résumé : This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning [D'après le résumé de l'éditeur]. En ligne : https://link.springer.com/book/10.1007/978-3-031-32879-4 Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177603 Deep Learning Foundations [texte imprimé] / Jo Taeho . - Cham : Springer Nature Switzerland, 2023 . - xx-426 p.
ISBN : 978-3-031-32881-7
Langues : Anglais (eng) Langues originales : Anglais (eng)
Descripteurs : Apprentissage automatique , Réseaux de neurones Index. décimale : 216 Statistique computationnelle Résumé : This book provides a conceptual understanding of deep learning algorithms. The book consists of the four parts: foundations, deep machine learning, deep neural networks, and textual deep learning. The first part provides traditional supervised learning, traditional unsupervised learning, and ensemble learning, as the preparation for studying deep learning algorithms. The second part deals with modification of existing machine learning algorithms into deep learning algorithms. The book’s third part deals with deep neural networks, such as Multiple Perceptron, Recurrent Networks, Restricted Boltzmann Machine, and Convolutionary Neural Networks. The last part provides deep learning techniques that are specialized for text mining tasks. The book is relevant for researchers, academics, students, and professionals in machine learning [D'après le résumé de l'éditeur]. En ligne : https://link.springer.com/book/10.1007/978-3-031-32879-4 Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177603 Réservation
Réserver ce document
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité E020848 216 TAE Ouvrage ENSAE 2. Statistique Disponible
Titre : Stochastic processes with R Type de document : texte imprimé Auteurs : Olga Korosteleva Editeur : Boca Raton, FL ; Londres : Chapman and Hall/CRC Press Année de publication : 2022 Collection : Chapman & Hall/CRC Texts in statistical science Importance : x-190 p. ISBN/ISSN/EAN : 978-1-03-215473-2 Langues : Anglais (eng) Langues originales : Anglais (eng) Descripteurs : Logiciel statistique R , Processus stochastique Index. décimale : 172 Fonctions stochastiques Résumé : Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses.
Key Features
- Provides complete R codes for all simulations and calculations
- Substantial scientific or popular applications of each process with occasional statistical analysis
- Helpful definitions and examples are provided for each process
- End of chapter exercises cover theoretical applications and practice calculations
[D'après le résumé de l'éditeur]En ligne : https://www.routledge.com/Stochastic-Processes-with-R-An-Introduction/Korostelev [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177621 Stochastic processes with R [texte imprimé] / Olga Korosteleva . - Boca Raton, FL ; Londres : Chapman and Hall/CRC Press, 2022 . - x-190 p.. - (Chapman & Hall/CRC Texts in statistical science) .
ISBN : 978-1-03-215473-2
Langues : Anglais (eng) Langues originales : Anglais (eng)
Descripteurs : Logiciel statistique R , Processus stochastique Index. décimale : 172 Fonctions stochastiques Résumé : Stochastic Processes with R: An Introduction cuts through the heavy theory that is present in most courses on random processes and serves as practical guide to simulated trajectories and real-life applications for stochastic processes. The light yet detailed text provides a solid foundation that is an ideal companion for undergraduate statistics students looking to familiarize themselves with stochastic processes before going on to more advanced courses.
Key Features
- Provides complete R codes for all simulations and calculations
- Substantial scientific or popular applications of each process with occasional statistical analysis
- Helpful definitions and examples are provided for each process
- End of chapter exercises cover theoretical applications and practice calculations
[D'après le résumé de l'éditeur]En ligne : https://www.routledge.com/Stochastic-Processes-with-R-An-Introduction/Korostelev [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177621 Réservation
Réserver ce document
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité E020849 172 KOR Ouvrage ENSAE 1. Mathématiques Disponible
Titre : The Machine Learning Solutions Architect Handbook : Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI Type de document : texte imprimé Auteurs : David Ping Mention d'édition : Second Edition Editeur : Birmingham (GBR) ; Olton (GBR) : Packt Publishing Année de publication : 2024 Importance : xxiv-574 p. ISBN/ISSN/EAN : 978-1-80512-250-0 Langues : Anglais (eng) Langues originales : Anglais (eng) Descripteurs : Apprentissage automatique , Entreprise , Intelligence artificielle Index. décimale : 88 Intelligence artificielle Résumé : You will learn to :
- Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture
- Gain an understanding of AI risk management frameworks and techniques
- Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency
- Create a business application using artificial intelligence services and custom models
- Dive into generative AI with use cases, architecture patterns, and RAG
[D'après l'éditeur]En ligne : https://www.packtpub.com/en-fr/product/the-machine-learning-solutions-architect- [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177619 The Machine Learning Solutions Architect Handbook : Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI [texte imprimé] / David Ping . - Second Edition . - Birmingham (GBR) ; Olton (GBR) : Packt Publishing, 2024 . - xxiv-574 p.
ISBN : 978-1-80512-250-0
Langues : Anglais (eng) Langues originales : Anglais (eng)
Descripteurs : Apprentissage automatique , Entreprise , Intelligence artificielle Index. décimale : 88 Intelligence artificielle Résumé : You will learn to :
- Apply ML methodologies to solve business problems across industries Design a practical enterprise ML platform architecture
- Gain an understanding of AI risk management frameworks and techniques
- Build an end-to-end data management architecture using AWS Train large-scale ML models and optimize model inference latency
- Create a business application using artificial intelligence services and custom models
- Dive into generative AI with use cases, architecture patterns, and RAG
[D'après l'éditeur]En ligne : https://www.packtpub.com/en-fr/product/the-machine-learning-solutions-architect- [...] Permalink : https://genes.bibli.fr/index.php?lvl=notice_display&id=177619 Réservation
Réserver ce document
Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité e020847 88 PIN Ouvrage ENSAE 8. Informatique - Traitement de l'information Disponible
