ترجمه فارسی مقاله شبکه عصبی مصنوعی و یادگیری عمیق: مبانی و نظریه

• کتاب صدها نکته فارسی (خودمونی) – نسخه PDF — زبان ساده و کاربردی
  مشاهده نمونه نسخه نکات ساده
• کتاب صدها نکته رسمی فارسی – نسخه PDF — نگارش استاندارد و علمی
  مشاهده نمونه نسخه نکات رسمی
• کتاب صدها پرسش و پاسخ تشریحی – نسخه PDF
  — هر سؤال همراه با پاسخ کامل برای درک عمیق مفاهیم
  مشاهده نمونه نسخه پرسش و پاسخ
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه یادگیری سریع
  — پاسخ‌ها بلافاصله بعد از سؤال برای مرور سریع
  مشاهده نمونه نسخه کوییز سریع
• کتاب پرسش و پاسخ چهارگزینه‌ای – نسخه خودآزمایی
  — پاسخ‌ها در انتهای بخش‌ها برای سنجش واقعی یادگیری
  مشاهده نمونه نسخه آزمونی

🎯 این بسته یک دورهٔ آموزشی کامل و چندلایه است؛ شامل ویدیوهای آموزشی، کتاب‌ها، تمرین‌ها و خودآزمایی.

چکیده

"Artificial Neural Network and Deep Learning: Fundamentals and Theory" offers a comprehensive exploration of the foundational principles and advanced methodologies in neural networks and deep learning. This book begins with essential concepts in descriptive statistics and probability theory, laying a solid groundwork for understanding data and probability distributions. As the reader progresses, they are introduced to matrix calculus and gradient optimization, crucial for training and fine-tuning neural networks. The book delves into multilayer feed-forward neural networks, explaining their architecture, training processes, and the backpropagation algorithm. Key challenges in neural network optimization, such as activation function saturation, vanishing and exploding gradients, and weight initialization, are thoroughly discussed. The text covers various learning rate schedules and adaptive algorithms, providing strategies to optimize the training process. Techniques for generalization and hyperparameter tuning, including Bayesian optimization and Gaussian processes, are also presented to enhance model performance and prevent overfitting. Advanced activation functions are explored in detail, categorized into sigmoid-based, ReLU-based, ELU-based, miscellaneous, non-standard, and combined types. Each activation function is examined for its properties and applications, offering readers a deep understanding of their impact on neural network behavior. The final chapter introduces complex-valued neural networks, discussing complex numbers, functions, and visualizations, as well as complex calculus and backpropagation algorithms. This book equips readers with the knowledge and skills necessary to design, and optimize advanced neural network models, contributing to the ongoing advancements in artificial intelligence.

چکیده به فارسی (ترجمه ماشینی)