estricted Boltzmann machines (RBMs) have been used as generative models of many different types of data including labeled or unlabeled images (Hinton et al., 2006a), windows of mel-cepstral coefficients that represent speech (Mohamed et al., 2009),
Contents 1 Introduction 2 1.1 How do We Train Deep Architectures? 5 1.2 Intermediate Representations: Sharing Features and Abstractions Across Tasks 7 1.3 Desiderata for Learning AI 10 1.4 Outline of the Paper 11 2 Theoretical Advantages of Deep Arc
Deep learning .................................................. 417 10.1 Deep Feedforward Networks ...................................................420 The MNIST Evaluation ........................................................... 421 Losses an
This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architecture
深度学习的代码It provides deep learning tools of deep belief networks (DBNs) of stacked restricted Boltzmann machines (RBMs). It includes the Bernoulli-Bernoulli RBM, the Gaussian-Bernoulli RBM, the contrastive divergence learning for unsupervised pre-train
