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
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
, such as the right half, should be added.Unefficient Grid Size reductionThere is a problem, it will increase the computational capacity, so szegedy came up with the following pooling layer.Efficient Grid Size reductionAs you can see, Szegedy uses two parallel structures to complete the grid size reduction, respectively, the right half of the conv and pool. The left half is the inner structure of the right part.Why did you do this? I mean, how is this structure designed? Szegedy no mention, perh
1. Research background and rationale
1958, Rosenblatt proposed Perceptron model (ANN)In 1986, Hinton proposed a deep neural network with multiple hidden layers (MNN)In the 2006, Hinton Advanced Confidence Network (DBN), which became the main frame of deep learning.Then, the efficiency of this algorithm is validated by Bengio Experiment 2.3 classes of depth learning
Deep learning GroupsSome Labs and groups that is actively working on deep learning:University of Toronto-machine Learning Group (Geoffrey Hinton, Rich Zemel, Ruslan Salakhutdinov, Brendan Frey, Radford N EalUniversitéde montréal– MILA Lab (Yoshua Bengio, Pascal Vincent, Aaron Courville, Roland Memisevic)New York univer
7.27 after the summer vacation, I started to run the deep learning program after I completed the financial project.
Hinton ran the article code on nature for three days, and then DEBUG changed the batch from 200 to 20.
Later, I started reading articles and felt dizzy.
It turns to: Deep Learning tutorials installs thean
The Wunda "Deep learning engineer" Special course includes the following five courses:
1, neural network and deep learning;2, improve the deep neural network: Super parameter debugging, regularization and optimization;3. Structured machine
Deep Q Network
4.1 DQN Algorithm Update
4.2 DQN Neural Network
4.3 DQN thinking decision
4.4 OpenAI Gym Environment Library
Notesdeep q-learning algorithmThis gives us the final deep q-learning algorithm with experience Replay:There is many more tricks this DeepMind used to actually make it wo
Source: http://www.teglor.com/b/deep-learning-libraries-language-cm569Python
Theano is a Python library for defining and evaluating mathematical expressions with numerical arrays. It makes it easy-to-write deep learning algorithms in Python. The top of the Theano many more libraries is built.
kerasis
0. OriginalDeep learning algorithms with applications to Video Analytics for A Smart city:a Survey1. Target DetectionThe goal of target detection is to pinpoint the location of the target in the image. Many work with deep learning algorithms has been proposed. We review the following representative work:SZEGEDY[28] modified the
difficult to benefit from end-to-end learning methods;
The DCF algorithm is less than two: Model updating adopts the method of sliding weighted averaging, which is not the optimal updating method, because once the noise is involved in the update, it is likely to lead to the drift of the model, so it is difficult to simultaneously get the stability and adaptability of the model.
Improvement One: The model of DCF algorithm is regarded as convolution fi
July algorithm December machine learning online Class---20th lesson notes---deep learning--rnnJuly algorithm (julyedu.com) December machine Learning Online class study note http://www.julyedu.com
Cyclic neural networks
Before reviewing the knowledge points:Fully connected forward network:
Some of the material of the deep learning introductory study are summarized according to the answers of some of Daniel's replies:Be noted that SOME VIDEOS is on youtube! I believe that you KNOW how to ACESS them.1. Andrew Ng's machine learning contents of the first four chapters (linear regression and logistic regression)Http://open.163.com/special/opencourse/mac
similar to the dimensionality reduction) method. Maximum pooling divides the input image into overlapping image matrix blocks, and each sub-region outputs its maximum value. The two reasons why the maximum pooling method is very effective in the visual processing problem are:(1) Reduce the computational complexity of the upper level by reducing the non-maximum value.(2) The result of pooling supports translation invariance. In the convolution layer, each pixel point has 8 orientations that can
Deep learning--the artificial neural network and the upsurge of researchHu XiaolinThe artificial neural network originates from the last century 40 's, to today already 70 years old. Like a person's life, has experienced the rise and fall, has had the splendor, has had the dim, has had the noisy, has been deserted. Generally speaking, the past 20 years of artificial neural network research tepid, until the
Https://github.com/exacity/deeplearningbook-chinese
In the help of many netizens and proofreading, the draft slowly became a first draft. Although there are still many problems, at least 90% of the content is readable and accurate. We kept the meaning of the original book Deep learning as much as possible and kept the original book's statement.
However, we have limited levels and we cannot eliminate the va
Python1. Theano is a Python class library that uses array vectors to define and calculate mathematical expressions. It makes it easy to write deep learning algorithms in a python environment. On top of it, many classes of libraries have been built.1.Keras is a compact, highly modular neural network library that is designed to reference torch, written in Python, to support the invocation of GPU and CPU-optim
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
Neural network and support vector machine for deep learningIntroduction: Neural Networks (neural network) and support vector machines (SVM MACHINES,SVM) are the representative methods of statistical learning. It can be thought that neural networks and support vector machines both originate from the Perceptual machine (Perceptron). Perceptron is a linear classification model invented by Rosenblatt in the 195
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