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Udacity Android Learning Note: Lesson 4 Part A

Udacity Android Learning Note: Lesson 4 Part A/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968Disclaimer: This document is licensed under the following protocols: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0, reproduced please specify the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/

Udacity Google Deep Learning learning Notes

1. Why add pooling (pooling) to the convolutional networkIf you only use convolutional operations to reduce the size of the feature map, you will lose a lot of information. So think of a way to reduce the volume of stride, leaving most of the information, through pooling to reduce the size of feature map.Advantages of pooling:1. Pooled operation does not increase parameters2. Experimental results show that the model with pooling is more accurateDisadvantages of pooling:1. Because the stride of t

Udacity Android Learning Note: Lesson 4 Part B

Udacity Android Learning Note: Lesson 4 Part B/titer1/archimedes of dry Goods shop choresSource: Https://code.csdn.net/titer1Contact: 13,073,161,968Disclaimer: This document is licensed under the following agreement: Free reprint-Non-commercial-non-derivative-retention Attribution | Creative Commons by-nc-nd 3.0, reproduced please specify the author and source.Tips:https://code.csdn.net/titer1/pat_aha/blob/

The basic concept and code realization of "reinforcement learning" reinforcement learning

Selected from deeplearning4j the heart of the machine compiles participation: Nurhachu Null, Li Zenan From AlphaGo to autonomous cars, we can find intensive learning in many of the most advanced AI applications. This technology is how to start from scratch to learn to complete the task, the growth of "beyond the human level" of experts. This article will be a brief introduction. Neural networks have created recent breakthroughs in areas such as comp

Intensive learning and learning notes--Introducing intensive learning (reinforcement learning)

As we all know, when Alphago defeated the world go champion Li Shishi, the whole industry is excited, more and more scholars realize that reinforcement learning is a very exciting in the field of artificial intelligence. Here I will share my intensive learning and learning notes. The basic concept of

Learning reinforcement Learning (with Code, exercises and Solutions) __reinforcement

Why Study Reinforcement Learning Reinforcement Learning is one of the fields I ' m most excited about. Over the past few years amazing results like learning to play Atari Games from Raw Pixelsand Mastering the Game of Go have Gotten a lot of attention, but RL is also widely

Intensive learning (deep reinforcement learning) resources

it), in fact, he also do Chinese recognition (I was stunned). Or 2011, Abtahi and other people [3] with DBN to replace the traditional reinforcement learning in the approximation (do RL is not very kind, and deep mind on a little bit!) There is wood to feel very pity, almost all touched the door of nature),. 2012, Lange[4] This person further began to do the application, put forward deep fitted Q

Read and understand the reinforcement learning behind Alphago _alphago

Author | Joshua Greavescompiling | Liu Chang, Lin Yu 眄 This paper is the most important content in the book "Reinforcement Learning:an Introduction", which aims to introduce the basic concept and principle of learning reinforcement learning, so that readers can realize the newest model as soon as possible. After all, f

Learning reinforcement Learning (with Code, exercises and Solutions) __reinforcement

Why Study Reinforcement Learning Reinforcement Learning is one of the fields I ' m most excited about. Over the past few years amazing results like learning to play Atari Games from Raw Pixelsand Mastering the Game of Go have Gotten a lot of attention, but RL is also widely

Reinforcement Learning (iv) solving with Monte Carlo method (MC)

In reinforcement Learning (iii) using dynamic programming (DP), we discuss the method of solving the problem of reinforcement learning prediction and control problem by dynamic programming. However, since dynamic programming requires the value of a state to be updated each time, it goes back to all possible subsequent

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow

Learning notes TF053: Recurrent Neural Network, TensorFlow Model Zoo, reinforcement learning, deep forest, deep learning art, tf053tensorflow Recurrent Neural Networks. Bytes. Natural language processing (NLP) applies the network model. Unlike feed-forward neural network (FNN), cyclic networks introduce qualitative loo

Deep reinforcement learning bubbles and where is the road?

first, deep reinforcement learning of the bubbleIn 2015, DeepMind's Volodymyr Mnih and other researchers published papers in the journal Nature Human-level control through deep reinforcement learning[1], This paper presents a model deep q-network (DQN), which combines depth learnin

Introduction to Reinforcement learning first, Markov decision process

Introduction to Reinforcement learning first, Markov decision process The formation of reinforcement learning algorithm theory can be traced back to the 780 's, in recent decades the reinforcement learning algorithm has been silen

Enhanced Learning Reinforcement Learning Classic Algorithm Comb 2: Monte Carlo method

1 PrefaceIn the previous article, we introduced the two basic algorithms of policy iteration and value iteration based on the Bellman equation, but these two algorithms are actually difficult to apply directly, because the two algorithms are still biased to the idealized one. You need to know the state transition probability, and you need to traverse all the states. For the traversal state, of course, we can not do a full traversal, but only as far as possible through the exploration to the vari

Deep reinforcement learning--dqn_ depth Learning

Contact Way: 860122112@qq.com DQN (Deep q-learning) is a mountain of deep reinforcement learning (Deep reinforcement LEARNING,DRL), combining deep learning with intensive learning to ac

Repost:deep Reinforcement Learning

From:http://wanghaitao8118.blog.163.com/blog/static/13986977220153811210319/Accessed 2016-03-10Intensive Learning (deep reinforcement learning) resourcesGoogle's deep-mind team published a bull X-ray article in Nips in 2013, which blinded many people and unfortunately I was in it. Some time ago collected a lot of information about this, has been lying in the coll

Paper notes: Deep reinforcement learning with Double q-learning

Deep reinforcement learning with Double q-learningGoogle DeepMind  AbstractThe mainstream q-learning algorithm is too high to estimate the action value under certain conditions. In fact, it was not known whether such overestimation was common, detrimental to performance, and whether it could be organized from the main body. This article answers the above question

Reinforcement Learning Intensive Learning Series IV: Sequential differential td__ Intensive learning

Introduction The previous one is about Monte Carlo's reinforcement learning method, Monte Carlo reinforcement Learning algorithm overcomes the difficulty of model unknown to strategy estimation by considering the sampling trajectory, but the Monte Carlo method has the disadvantage that it is necessary to update the st

Learning roadmap of deep reinforcement learning

1. A series of articles about getting started with DQN:DQN from getting started to giving up2. Introductory Paper2.1 Playing Atariwith a deep reinforcement learning DeepMind published in Nips 2013, the first time in this paper Reinforcement learning this name, and proposed DQN (deep q-network) algorithm, realized from

Enhancement Learning reinforcement learning classical algorithm combing 3:TD method

Bellman equation is a solution to the ideal condition, and these methods are the achievable methods that are formed by abandoning the ideal accuracy.SummaryThis paper combs several TD-related algorithms. TD Algorithms in particular t d ( λ ) The method leads to the eligibility trace (the translation does not know whether the qualification trail), this part of the content to be analyzed later.StatementThe pictures of this article are captured from:1

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