Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
Top Free Unix/Linux Books 🌠 - 100% Free or Open Source!
  • Title: Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory
  • Author(s) Arnulf Jentzen, Benno Kuckuck, Philippe von Wurstemberger
  • Publisher: arXiv (October 31, 2023); eBook (arXiv Licensed)
  • License(s): arXiv - Non-exclusive license to distribute
  • Paperback: N/A
  • eBook: PDF (601 pages) and PostScript
  • Language: English
  • ISBN-10/ASIN: N/A
  • ISBN-13: N/A
  • Share This:  

Book Description

This book aims to provide an introduction to the topic of deep learning algorithms. We review essential components of deep learning algorithms in full mathematical detail including different Artificial Neural Network (ANN) architectures (such as fully-connected feedforward ANNs, convolutional ANNs, recurrent ANNs, residual ANNs, and ANNs with batch normalization) and different optimization algorithms (such as the basic Stochastic Gradient Descent (SGD) method, accelerated methods, and adaptive methods).

About the Author(s)
  • N/A
Reviews, Ratings, and Recommendations: https://www.youtube.com/watch?v=IULwA0UNJP8 Related Book Categories: Read and Download Links: Similar Books:
  • Mathematical Analysis of Machine Learning Algorithms (Tong Zhang)

    This self-contained textbook introduces students and researchers of AI to the main mathematical techniques used to analyze machine learning algorithms, with motivations and applications.

  • Neural Networks and Deep Learning (Michael Nielsen)

    Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. This book will teach you the core concepts behind neural networks and deep learning.

  • Deep Learning (Ian Goodfellow, et al)

    Written by three experts, this is the only comprehensive book on the subject. It offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning.

  • Machine Learning with Neural Networks (Bernhard Mehlig)

    This modern and self-contained book offers a clear and accessible introduction to the important topic of machine learning with neural networks. It provides comprehensive coverage of neural networks, their evolution, their structure, their applications, etc.

  • Neural Network Learning: Theoretical Foundations

    This book describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions.

  • Mathematics for Machine Learning (Marc P. Deisenroth, et al.)

    This self contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It provides a beautiful exposition of the mathematics underpinning modern machine learning.

  • Mathematics for CS and Machine Learning (Jean Gallier, et al.)

    Covering everything you need to know about machine learning, now you can master the mathematics, computer science and statistics behind this field and develop your very own neural networks!

  • Dive into Deep Learning (Aston Zhang, et al.)

    This is an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, now supports the TensorFlow, PyTorch, and Apache MXNet programming frameworks, drafted entirely through Jupyter notebooks.

  • Deep Learning for Coders with Fastai and PyTorch

    This book show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.

  • Foundations of Machine Learning (Mehryar Mohri, et al)

    This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

  • Understanding Machine Learning: From Theory to Algorithms

    This book explains the principles behind the automated learning approach and the considerations underlying its usage. It provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations.

  • The Hundred-Page Machine Learning Book (Andriy Burkov)

    Everything you really need to know in Machine Learning in a hundred pages! This book provides a great practical guide to get started and execute on ML within a few days without necessarily knowing much about ML apriori.

Book Categories
:
Other Categories
Resources and Links