… deep supervised learning (also recapitulating the history of backpropagation), unsupervised
learning, reinforcement learning & … for short programs encoding deep and large networks. …

… We’ll learn the core principles behind neural networks and deep learning by attacking a
concrete problem: the problem of teaching a computer to recognize handwritten digits. This …

… experimentation with more sophisticated and deep neural architectures than was previously
… of the potential of deep learning. This book discusses neural networks from this modern …

… Finally, we consider how deep neural network models might help us understand brain …
Deep learning by backpropagation Say we want to train a deep neural network model with …

… Many of the deep learning techniques utilizes the neural network architectures, to classify a
… referred as the deep neural networks. In deep neural networks the word deep points to the …

… This literature review provides an insight of deep learning neural networks. Based on the
applications and the design methods, deep networks are analysed and categorized into …

… This review summarizes machine learning and deep learning … learning (ML). ML contains
many models and methods, including deep learning (DL) and artificial neural networks (ANN). …

… conventional shallow machine learning. By contrast, this study explores a novel convolutional
neural network (CNN)-based approach that incorporates a deep-learning method to …

… 3, we discuss various artificial neural networks and deep learning methods and their
applicability within the area of cybersecurity. Several research issues and potential solutions based …

… In this paper, we discuss some widely-used deep learning architectures and their … deep
learning architectures, namely, autoencoder, convolutional neural network, deep belief network, …