The Fundamentals of Neural Networks and Deep Learning

“The Fundamentals of Neural Networks and Deep Learning” offers a comprehensive exploration of the core concepts and intricate mechanisms driving neural networks and deep learning. Covering a wide spectrum of topics from the basics of neural networks like perceptrons to the advanced models such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), this book provides an extensive overview of machine learning and deep learning paradigms.

Delving into techniques like restricted Boltzmann machines, word2vec, and autoencoders, it navigates through the terrain of deep reinforcement learning and generative adversarial networks. Exploring optimization algorithms including backpropagation, conjugate gradient descent, Adam, and RMSProp, it equips readers with the tools necessary for efficient model training and development. With a focus on both theoretical foundations and practical applications, this resource serves as an indispensable guide for anyone delving into the intricate realm of neural networks and deep learning.

Keywords

Deep Learning, Machine Learning, Radial Basis Function Networks, Restricted Boltzmann Machines, Recurrent Neural Networks, Convolutional Neural Networks, Neural Networks, Perceptron, Deep Reinforcement Learning, Word2Vec, Autoencoder, Logistic Regression, Dropout, Pretraining, Backpropagation, Conjugate Gradient-Descent, Adam, Rmsprop, Kohonean Self-Organizaing Map, Generative Adversarial Networks