Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
genuine image
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
Unlock the potential of time series data with deep learning expertise using PyTorch and Python.
Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection
Numéro d'article: 102283349

Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

76% des répondants recommanderaient ceci à un ami

Numéro d'article: 102283349

HTG 7531

Détails du prix

Hors frais de livraison et de douane ( Les frais de livraison et de douane seront calculés lors du paiement )

*Tous les articles seront importés depuis États-Unis

En stock
États-Unis Importé depuis la boutique USA

QTY:

Commandez maintenant et recevez votre commande aux alentours du Samedi, Octobre 10
Transaction sécuriséeTransaction sécurisée

Ubuy s'engage à protéger votre sécurité et votre confidentialité. Notre système avancé de sécurité des paiements garantit la confidentialité en chiffrant vos informations lors de la transmission grâce aux protocoles AES (Advanced Encryption Standards) et SSL (Secure Socket Layer). Vos coordonnées de paiement sont 100 % sécurisées car nous ne partageons pas vos informations de paiement avec des vendeurs tiers.

Nos meilleurs partenaires logistiques
  • fedex
  • dhl
Unlock the potential of time series data with deep learning expertise using PyTorch and Python.
Garantie U-Care :
Aucun
Sélectionnez un forfait
fast shipping

Livraison
rapide

free return

Retour
gratuit*

Emballage sécurisé

Emballage sécurisé

Produits 100 % originaux

Produits 100 % originaux

pci-dss

Conformité PCI DSS

iso certified

Certifié ISO 27001

paypal payment
visa payment
mastercard payment

Ce qui se démarque

Comprehensive Recipes
Offers a wide array of practical recipes tailored for time series forecasting, classification, and anomaly detection, ensuring hands-on learning through real-world applications.
Cutting-edge Framework
Utilizes the powerful PyTorch framework, providing advanced deep learning techniques that enable efficient model training and faster inference, suitable for both beginners and experts.
Focus on Actual Use
Addresses real-world challenges with practical solutions, helping users tackle common time series problems effectively and enhancing their predictive capabilities across various domains.

Détails du produit

Shop Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection online at a best price in Haiti. 1805129236
Publisher Packt Publishing
Publication date March 29, 2024
Edition 1st
Language English
Print length 274 pages
ISBN-10 1805129236
ISBN-13 978-1805129233
Item Weight 1.05 pounds (480 grams)
Dimensions 7.5 x 0.62 x 9.25 inches (19.1 x 1.6 x 23.5 cm)

À qui est-ce destiné ?

Suitable For
  • Data Scientists

    Ideal for data scientists looking to enhance their skills in deep learning and time series analysis using PyTorch.

  • Machine Learning Practitioners

    Perfect for practitioners wanting to implement advanced modeling techniques for forecasting and classification tasks.

  • Students and Learners

    Great for students who want practical, hands-on experience with time series analysis in Python and deep learning frameworks.

Not Suitable For
  • Beginners in Programming

    Not suitable for complete beginners in programming, as it assumes familiarity with Python and deep learning concepts.

DESCRIPTION DU PRODUIT

Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection

Vous avez une question ? Chattez avec nous

Questions et réponses des clients

  • question: What is the primary focus of 'Deep Learning for Time Series Cookbook'?

    répondre: The primary focus of 'Deep Learning for Time Series Cookbook' is to provide readers with practical recipes for implementing deep learning techniques specifically tailored for time series analysis. It covers a range of tasks, including forecasting, classification, and anomaly detection, making it a valuable resource for data scientists and machine learning practitioners. By using PyTorch and Python, the book emphasizes hands-on approaches and real-world applications, enabling users to effectively tackle complex time series problems in various industries such as finance, healthcare, and sales forecasting.
  • question: Who is the target audience for this cookbook?

    répondre: The target audience for 'Deep Learning for Time Series Cookbook' includes data scientists, machine learning engineers, researchers, and students interested in the application of deep learning techniques to time series problems. It is particularly beneficial for those with a foundational knowledge of Python and basic machine learning concepts. This cookbook format ensures that readers can easily follow along with practical recipes, making it suitable for both beginners looking to enter the field and experienced professionals seeking to enhance their skills in time series analysis.
  • question: Can beginners use this cookbook effectively?

    répondre: Yes, beginners can use 'Deep Learning for Time Series Cookbook' effectively, as it offers step-by-step recipes that guide readers through the implementation of various deep learning models. The book starts with foundational concepts before gradually introducing more complex techniques. Additionally, the use of clear explanations and supporting code examples allows novices to learn at their own pace while developing practical skills. This eases the learning curve and helps beginners build a solid understanding of time series analysis using deep learning.
  • question: What are some key topics covered in the cookbook?

    répondre: Key topics covered in 'Deep Learning for Time Series Cookbook' include forecasting techniques, various classification models tailored for time series data, and methods for anomaly detection. The book delves into recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and attention mechanisms, among other important concepts. Each topic is presented with practical recipes to guide users in implementing these techniques in real projects, making it a comprehensive reference for those looking to apply deep learning to time series data.
  • question: What programming languages and libraries are utilized in the cookbook?

    répondre: The 'Deep Learning for Time Series Cookbook' primarily utilizes Python as the programming language, focusing on the PyTorch library for building and training deep learning models. PyTorch is favored for its dynamic computational graph and intuitive interface, which ease experimentation and debugging. Readers will find that other relevant libraries, such as NumPy, pandas, and Matplotlib for data manipulation and visualization, complement the recipes presented. This integration provides a rich environment for developing and deploying time series models in Python.
  • question: How does the cookbook approach anomaly detection in time series data?

    répondre: The cookbook approaches anomaly detection in time series data through practical recipes that employ advanced deep learning techniques. It discusses models such as LSTM networks and Autoencoders specifically designed to identify outliers or irregular patterns in sequences of data. Through step-by-step guidance, users learn how to preprocess their data, train models, and interpret results for practical applications, such as fraud detection in financial transactions or monitoring manufacturing equipment for unusual behavior. This hands-on approach solidifies understanding and fosters confidence in handling real-world data.
  • question: Is this cookbook suitable for professionals in finance or healthcare?

    répondre: Absolutely, 'Deep Learning for Time Series Cookbook' is highly suitable for professionals in finance or healthcare, as these fields heavily rely on time series analysis for decision-making. In finance, practitioners can utilize the cookbook to forecast stock prices or analyze market trends. In healthcare, it can be employed for patient monitoring or predicting disease outbreaks based on historical data. The cookbook's practical recipes and real-world examples empower professionals to apply deep learning techniques effectively, enhancing their analytical capabilities in these critical sectors.
  • question: What types of forecasting techniques are discussed?

    répondre: The cookbook discusses various forecasting techniques that leverage deep learning to predict future values based on historical time series data. Techniques include traditional models like ARIMA and more advanced approaches involving recurrent neural networks (RNNs) and LSTMs configured for univariate and multivariate forecasting tasks. Each recipe includes practical implementation steps and highlights performance evaluation metrics, allowing readers to benchmark their models. This comprehensive coverage ensures users can choose the most appropriate technique for their specific forecasting needs.
  • question: What are some practical applications of the concepts in this cookbook?

    répondre: Practical applications of concepts from 'Deep Learning for Time Series Cookbook' are vast and span multiple industries. In e-commerce, businesses can forecast sales and optimize inventory levels. In finance, traders can develop models to analyze and predict stock market trends. In healthcare, institutions can monitor patient data to anticipate clinical events. By equipping readers with actionable techniques, the cookbook enables professionals to harness deep learning for improving operational efficiency, making informed decisions, and enhancing predictive analytics in various domains.
  • question: Where can I buy Deep Learning for Time Series Cookbook in Haiti?

    répondre: You can buy 'Deep Learning for Time Series Cookbook: Use PyTorch and Python recipes for forecasting, classification, and anomaly detection 1st Edition' on Ubuy in Haiti. Ubuy offers a convenient platform with a variety of products, making it easy for you to purchase the cookbook online. The site provides reliable access, ensuring you can obtain this essential resource for time series analysis using deep learning techniques.

Intelligence & Semantics Editorial Review

**** "Deep Learning for Time Series Cookbook" is a well-structured and practical guide that serves as an essential resource for those looking to master time series analysis through deep learning techniques. With a clear focus on using PyTorch, this book provides readers with the necessary tools to not only understand but also effectively implement advanced deep learning methods in real-world applications. The text begins by establishing the relevance of time series analysis across various industries, demonstrating how deep learning can enhance the ability to forecast trends, detect anomalies, and classify data. The author seamlessly integrates theoretical knowledge with hands-on coding recipes, making the complex subject matter more accessible and relatable. The approach is commendable for catering to a wide range of skill levels, starting from fundamental concepts and escalating to intricate deep learning architectures, including CNNs and transformers. Readers will find significant value in the detailed explanations and practical examples provided, which bridge the gap between theory and practice. The inclusion of diverse applications, such as univariate and multivariate forecasting, contributes to the book’s relevance for current data science professionals and researchers alike. The accompanying well-organized GitHub repository enhances the learning experience, reinforcing concepts and enabling readers to apply what they’ve learned efficiently. In summary, "Deep Learning for Time Series Cookbook" is not just a manual; it’s a comprehensive handbook for researchers, data scientists, and software engineers seeking to innovate within the realm of time series forecasting and analysis via deep learning. **

Avis et évaluations clients

1 évaluations des clients
  • 5 étoile
    100%
  • 4 étoile
    0%
  • 3 étoile
    0%
  • 2 étoile
    0%
  • 1 étoile
    0%

Donnez votre avis sur ce produit

Partagez votre avis avec d'autres clients

Avantages

  • Comprehensive and practical guide for time series analysis.
  • Easy-to-follow code recipes for various time series challenges.
  • Covers advanced topics, making it suitable for diverse skill levels.
  • Emphasizes the use of PyTorch and its libraries.
  • Includes real-world applications and examples.
  • Well-organized GitHub repository for reinforcement.

Les inconvénients

  • Requires some prior knowledge of Python and related libraries to fully benefit.

CONFIANCE EN LA PLATEFORME & PROTECTION DE L'ACHETEUR

trustpilot logo
4.2/5 9477 avis
Lire les avis
JK
Jasmin
Acheteur vérifié

“Great products and very good service: very easy and very fast international delivery.”

9 September 2026 · via Trustpilot
AG
Anke
Acheteur vérifié

“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”

10 September 2026 · via Trustpilot
H
Hazel
Acheteur vérifié

“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”

10 September 2026 · via Trustpilot
O
Opaleye
Acheteur vérifié

“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”

11 September 2026 · via Trustpilot
AC
Adele
Acheteur vérifié

“Easy to find and order what you want on the website. Delivery is quick to the UK”

8 September 2026 · via Trustpilot
Paiement sécurisé Global Delivery Retours faciles Genuine Products

Historique des prix du produit

Informations importantes

  • Limitations : Pour les produits expédiés à l'international, veuillez noter que toute garantie du fabricant peut ne pas être valide ; les options de service du fabricant peuvent ne pas être disponibles ; les manuels, instructions et avertissements de sécurité des produits peuvent ne pas être dans les langues du pays de destination ; les produits (et les matériaux qui les accompagnent) peuvent ne pas être conçus conformément aux normes, spécifications et exigences d'étiquetage du pays de destination ; et les produits peuvent ne pas être conformes à la tension et aux autres normes électriques du pays de destination (nécessitant l'utilisation d'un adaptateur ou d'un convertisseur le cas échéant). Il incombe au destinataire de s'assurer que le produit peut être importé légalement dans le pays de destination. En cas de commande auprès d'Ubuy ou de ses filiales, le destinataire est l'importateur officiel et doit se conformer à toutes les lois et réglementations du pays de destination.
  • Tous les produits listés sur Ubuy ne sont pas à vendre, Ubuy étant un moteur de recherche mondial. Les produits sont soumis aux réglementations en matière d'exportation et de commerce.

HTG 7531

Commandez maintenant et recevez votre commande aux alentours du Samedi, Octobre 10

QTY:

Transaction sécuriséeTransaction sécurisée

Conforme PCI DSS et certifié ISO 27001:2022, avec des paiements chiffrés et une protection complète de l'acheteur pour chaque commande.

Nos meilleurs partenaires logistiques
  • fedex
  • dhl
Livraison transfrontalière la plus rapide

Caractéristiques et avantages

  • Master time series analysis and modeling with deep learning.
  • Utilize PyTorch to build advanced deep neural networks.
  • Learn practical solutions for forecasting, classification, and anomaly detection.
  • Includes free PDF eBook with purchase of print or Kindle version.
  • Gain hands-on experience through easy-to-follow code recipes.
  • Ideal for machine learning enthusiasts with basic Python knowledge.

Assurance Ubuy

Profitez d'une expérience d'achat sereine avec des produits 100 % originaux, une sécurité de paiement conforme PCI DSS, une protection des données certifiée ISO 27001, la livraison transfrontalière la plus rapide, les retours gratuits* et un emballage sécurisé pour chaque commande.