machine learning and neural networks

The idea of a neural network is to train a non-linear function, which is usually applied to the following situations: When many factors are determined and complex, for example, the fire of a fire building may increase, which may be determined by the wind power at that time. , Temperature, surrounding environment, house structure, house facilities, etc. When we cannot get a correct answer based on these parameters In this case, we can use

neural network, it is necessary to control the number of trainable parameters of two networks, otherwise there is no comparability. In his machine learning video, Professor Li Hongyi, for example, deep performance is better with the same number of parameters, which means that deep parameters will be less if the same e

(logistic| Tanh), then the most common situation is $gradient \rightarrow 0$★ Biological Angle:The term is called long-term memory degraded to short-term memory, can only remember short-term memories.3.3 RNNLMAlthough simple RNN has many flaws, but short-term memory after all better than nothing.[MIKOLOV10] first proposed to use RNN to do LM, but did not use Word embedings.RNNLM from Sentence-level, a sentence as a sequence, one by one, word to push the timing.3.4 RNN for Speech understanding[M

Transfer from http://blog.csdn.net/xingzhedai/article/details/53144126More information: http://blog.csdn.net/mafeiyu80/article/details/51446558http://blog.csdn.net/caimouse/article/details/70225998http://kubicode.me/2017/05/15/Deep%20Learning/Understanding-about-RNN/RNN (recurrent Neuron) is a neural network for modeling sequence data. Following the bengio of the probabilistic language model based on neural

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logical policy. Artificial Neural Networks have obvious advantages in the following three aspects: 1. Self-learning 2. Lenovo Storage 3. fast search for optimization solutions For more information, see Aforge. NET Single-layer network implementation and operation The implementation of neural

. AI technology in game programming. .(Serialization II) 3Digital neural networks (the digital version) We have seen that the biological brain is composed of many neural cells. Similarly, the artificial neural network ANN that simulates the brain is composed of many artificial

, we can deduce it on paper by ourselves. The following is a summary of the implementation process of the BP algorithm (directly misappropriation Ng's diagram bar):the above is the details of the BP algorithm principle. In summary, it is:1. Use forward propagation to calculate the "activation value" of each layer. 2. Calculate the residual of each output unit of the last layer, namely the output layer. 3, calculate the residual of the first node. 4, calculate the partial derivative we need. spea

/RPjZvzsYou can buy the entire book "write your own neural network" with a meal! To purchase an e-book, you will get: 1. Face-to-face communication between QQ Group 96980352 and instructor Ge yiming! 2. One book that changes your fateAll-in-One neural networks by yourself! 3. With the Book source code, you can build your own

Awesome Recurrent neural NetworksA curated list of resources dedicated to recurrent neural networks (closely related to deep learning).Maintainers-jiwon Kim, Myungsub ChoiWe have pages for other topics:awesome-deep-vision, awesome-random-forestContributingPlease feel free-to-pull requests, email myungsub Choi ([e-Mail

the input terminal I is simply)Alpha factor:Alpha or E represents the annotation of the J-input word and the importance of the i-1 hidden state of the decoder end, so that the resulting CI will pay attention for some locations, equivalently as the translation word I to the original input some position pay AttetnionUsing BIRNN:This paper uses bidirectional rnn to catch the forward and backward hi stitching together, so that the annotation can represent the information around the input word I.Net

convolutional Neural Networks convolutional neural network contents One: Leading back propagation reverse propagation algorithm Network structure Learning Algorithms Two: convolutional neural networks convolut