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Applied Deep Learning Design and implement your own Neural Networks to solve real-world problems - Dr. Rajkumar Tekchandani, Dr. Neeraj Kumar 2023 RETAIL PDF BPB Publications BOOKS PROGRAMMING
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Applied Deep Learning Design and implement your own Neural Networks to solve real-world problems
Author: Dr. Rajkumar Tekchandani, Dr. Neeraj Kumar
Year: 2023
Pages: 624
Format: RETAIL PDF
File size: 27.0 MB
Language: ENG



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Book Description: Applied Deep Learning: Design and Implement Your Own Neural Networks to Solve Real-World Problems In today's world, data is being generated at an unprecedented rate, and the need to process and make sense of this data has become increasingly important. Deep learning has emerged as a powerful tool for extracting insights and making predictions from large datasets. This comprehensive guide is designed for beginners who want to gain a deeper understanding of the techniques and implementations of deep learning. Starting from the basics, the book progresses to a thorough coverage of deep learning with Python, providing readers with the intuition of neural networks and how to design and train them effectively.
Прикладное глубокое обучение: проектирование и внедрение собственных нейронных сетей для решения реальных проблем В современном мире данные генерируются с беспрецедентной скоростью, и необходимость обработки и осмысления этих данных становится все более важной. Глубокое обучение стало мощным инструментом для извлечения информации и прогнозирования из больших наборов данных. Это всеобъемлющее руководство предназначено для новичков, которые хотят глубже понять методы и реализации глубокого обучения. Начиная с основ, книга переходит к тщательному освещению глубокого обучения с помощью Python, предоставляя читателям интуицию нейронных сетей и способы их эффективного проектирования и обучения.
Deep arning appliqué : concevoir et mettre en œuvre ses propres réseaux neuronaux pour résoudre des problèmes réels Dans le monde d'aujourd'hui, les données sont générées à une vitesse sans précédent et la nécessité de les traiter et de les comprendre devient de plus en plus importante. L'apprentissage approfondi est devenu un outil puissant pour extraire des informations et prédire de grands ensembles de données. Ce guide complet est conçu pour les débutants qui veulent mieux comprendre les méthodes et la mise en œuvre de l'apprentissage profond. En commençant par les bases, le livre passe à une couverture approfondie de l'apprentissage profond avec Python, fournissant aux lecteurs l'intuition des réseaux neuronaux et les moyens de les concevoir et de les apprendre efficacement.
Aprendizaje profundo aplicado: diseño e implementación de redes neuronales propias para resolver problemas reales En el mundo actual, los datos se generan a una velocidad sin precedentes y la necesidad de procesar y comprender estos datos es cada vez más importante. aprendizaje profundo se ha convertido en una poderosa herramienta para extraer información y predecir desde grandes conjuntos de datos. Esta guía integral está diseñada para los principiantes que desean comprender más a fondo los métodos y las implementaciones de aprendizaje profundo. A partir de los fundamentos, el libro pasa a una cuidadosa iluminación del aprendizaje profundo con Python, proporcionando a los lectores la intuición de las redes neuronales y formas de diseñarlas y enseñarlas de manera efectiva.
Treinamento profundo aplicado: Projetar e implementar suas próprias redes neurais para resolver problemas reais No mundo atual, os dados são gerados a uma velocidade sem precedentes, e a necessidade de processar e compreender esses dados é cada vez mais importante. A aprendizagem profunda tornou-se uma ferramenta poderosa para extrair informações e prever de grandes conjuntos de dados. Este guia abrangente é destinado aos novatos que querem compreender melhor os métodos e a implementação do aprendizado profundo. A partir dos fundamentos, o livro passa a cobrir o aprendizado profundo com Python, fornecendo aos leitores o intuito das redes neurais e as formas de projetá-las e aprendê-las de forma eficiente.
Applied Deep arning: Design und Implementierung eigener neuronaler Netzwerke zur Lösung realer Probleme In der heutigen Welt werden Daten mit beispielloser Geschwindigkeit erzeugt, und die Notwendigkeit, diese Daten zu verarbeiten und zu verstehen, wird immer wichtiger. Deep arning ist zu einem leistungsstarken Werkzeug geworden, um Informationen aus großen Datensätzen zu extrahieren und vorherzusagen. Dieser umfassende itfaden richtet sich an Anfänger, die ein tieferes Verständnis der Techniken und Implementierungen von Deep arning wünschen. Ausgehend von den Grundlagen geht das Buch zu einer gründlichen Berichterstattung über Deep arning mit Python über, die den sern die Intuition neuronaler Netzwerke und Möglichkeiten bietet, sie effektiv zu entwerfen und zu lernen.
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Uygulamalı Derin Öğrenme: Gerçek Problemleri Çözmek için Kendi nir Ağlarınızı Tasarlama ve Uygulama Günümüz dünyasında, veriler benzeri görülmemiş bir oranda üretilmekte ve bu verileri işleme ve anlama ihtiyacı giderek daha önemli hale gelmektedir. Derin öğrenme, büyük veri kümelerinden bilgi çıkarmak ve tahmin etmek için güçlü bir araç haline gelmiştir. Bu kapsamlı kılavuz, derin öğrenme teknikleri ve uygulamaları hakkında daha derin bir anlayış kazanmak isteyen yeni başlayanlar için tasarlanmıştır. Temel bilgilerden başlayarak, kitap Python'u kullanarak derin öğrenmenin kapsamlı bir kapsamına geçerek, okuyuculara sinir ağlarının sezgisini ve bunları verimli bir şekilde tasarlama ve öğrenme yollarını sunar.
التعلم العميق التطبيقي: تصميم وتنفيذ شبكتك العصبية الخاصة لحل المشكلات الحقيقية في عالم اليوم، يتم إنشاء البيانات بمعدل غير مسبوق، وتزداد أهمية الحاجة إلى معالجة وفهم هذه البيانات. أصبح التعلم العميق أداة قوية لاستخراج المعلومات والتنبؤ من مجموعات البيانات الكبيرة. هذا الدليل الشامل مخصص للمبتدئين الذين يرغبون في اكتساب فهم أعمق لتقنيات التعلم العميق والتطبيقات. بدءًا من الأساسيات، ينتقل الكتاب إلى تغطية شاملة للتعلم العميق باستخدام Python، مما يوفر للقراء حدس الشبكات العصبية وطرق تصميمها وتعلمها بكفاءة.

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