Title The Future of Machine Intelligence: Perspectives from Leading Practitioners Author(s) David Beyer Publisher: O’Reilly Media Inc. (February 29, 2016) Book Descr iption Advances in both theory and practice are throwing the promise of machine lea
不多说，圣经级别的书籍。差积分，不得不贱卖。 This book would not have been possible without the contributions of many people. We would like to thank those who commented on our proposal for the book and helped plan its contents and organization: Guillaume Alain, Kyunghyun
James Martens JMARTENS @ CS . TORONTO . EDU Ilya Sutskever ILYA @ CS . UTORONTO . CA University of Toronto, Canada Abstract In this work we resolve the long-outstanding problem of how to effectively train recurrent neu- ral networks (RNNs) on comple
One-Shot Imitation Learning arXiv:1703.07326v3 [cs.AI] 4 Dec 2017 Yan Duan , Marcin Andrychowicz , Bradly Stadie , Jonathan Ho , Jonas Schneider , Ilya Sutskever , Pieter Abbeel , Wojciech Zaremba
用最简单的模型、最简单的特征工程做出好效果，追求的就是极致性价比。如果有需要，可以在此基础上做一些模型更改和特征工程，提高表现效果。ture for face verification developed by Chopra, Hadsell, and This forces the LSTm to entirely capture the semantic dif-
LeCun(2005), which utilizes symmetric Conv Nets where ferences d
关于Hindsight Experience Replay的原始论文，适合初学者对深度强化学习Hindsight Experience Replay的认识和了解is to periodically set the weights of the target network to the current weights of the main network(e. g
Mnih et al. (2015)) or to use a polyak-averaged(Polyak and Judits
关于duelingdqn的原始论文，适合初学者对深度强化学习duelingdqn的认识和了解Dueling Network Architectures for Deep Reinforcement Learning
et al.(2016). The results of Schaul et al.(2016) are the 2.1. Deep Q-networks
current published state-of-the-art
The value functions as descri
一种FPGA硬件加速方案，实现深度学习，可实现高吞吐量的CNN网络Session 3: Deep Learning
FPGA 18, February 25-27, Monterey, CA, USA
maps. Let b, n and m index into the Batch, fin and fout dimensions
Table 1: Variation of model paramcters
Equation 4 specifies the operations of a co
语音识别LAS结构where d and y, are MLP networks. After training, the a; distribution Table 1: WER comparison on the clean and noisy Google voice
is typically very sharp and focuses on only a few frames of h; ci car
search task. The CLDNN-hMM system is the s
