[R] (Very detailed) Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Arxiv: [https://arxiv.org/abs/2310.20360](https://arxiv.org/abs/2310.20360)

601 pages, 36 figures, 45 source codes

>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). We also cover several …

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