neural network tutorial python

Source: Michael Nielsen's "Neural Network and Deep learning", click the end of "read the original" To view the original English.This section translator: Hit Scir master Li ShengyuDisclaimer: If you want to reprint please contact [email protected], without authorization not reproduced. Using neural networks to recognize handwritten numbers How

= 0.01022026918051116\]We take the study rate\ (\eta=0.5\), using the formula\[{w_{1,1}}_{new}=w_{1,1}-\eta \frac{\partial e}{\partial w_{1,1}}\]After getting the updated\ ({w_{1,1}}_{new}\)For:\[{w_{1,1}}_{new}=0.9-0.5 \times 0.01022026918051116=0.191611086576=0.89488986540974442\]The same method can update the values of other weights. In this way, we have completed the introduction of the error back propagation algorithm, in the actual training we continue to iterate through this method, unti

http://blog.csdn.net/diamonjoy_zone/article/details/70576775Reference:1. inception[V1]: going deeper with convolutions2. inception[V2]: Batch normalization:accelerating deep Network Training by reducing Internal covariate Shift3. inception[V3]: Rethinking the Inception Architecture for computer Vision4. inception[V4]: inception-v4, Inception-resnet and the Impact of residual Connections on learning1. PrefaceThe NIN presented in the previous article ma

Something hot is obviously going to cool. The room will get messy and frustrating. Almost the same, the message is distorted. The short-term strategy for reversing these conditions is to reheat, do the sanitation and use the Hopfield network respectively. This article introduces you to the last of the three, which is an algorithm that eliminates noise only if you need a specific parameter. Net.py is a particularly simple

://www.ibm.com/developerworks/cn/java/j-lo-robocode3/index.htmlArtificial Intelligence Java Tank Robot Series: neural Network, lowerhttp://www.ibm.com/developerworks/cn/java/j-lo-robocode4/Using Python to construct a neural network--hopfield

In this paper, a simple handwriting recognition system is realized by BP neural network.First, the basic knowledge1 environmentpython2.7Need to numpy and other librariesCan be installed with sudo apt-get install python-2 Neural Network principleHttp://www.hankcs.com/ml/back-propagation-

Constructing neural network with Keras Keras is one of the most popular depth learning libraries, making great contributions to the commercialization of artificial intelligence. It's very simple to use, allowing you to build a powerful neural network with a few lines of code. In this article, you will learn how to bui

The basic overview of neural networks and neural network models are not carefully introduced here. A detailed introduction to the introduction of the neural network and its model is presented in the details of Daniel Ng, Stanford University. This paper mainly introduces the

bottom, down to top. The default is LR. Example: Drawing a lenet model # sudo python python/draw_net.py examples/mnist/lenet_train_test.prototxt netimage/lenet.png--rankdir=TB 　　　　　　 3. Summary The graph drawn with Netscope is simple and easy to understand the network model quickly, but lacks the detail information in the layer.The structure diagram drawn with

the exit string, the connection is closed directly.To test this server program, we also need to write a client program:Note that the client program runs out, and the server program will run forever, you must press CTRL + C to exit the program.SummarySocket programming with the TCP protocol is very simple in Python, for the client, to actively connect to the server's IP and the specified port, for the server, to first listen to the specified port, and

REF: Convolution neural network CNNs from LeNet-5The qac of some of the posts in this article:1. FundamentalsMLP (Multilayer Perceptron, multilayer perceptron) is a forward neural network (as shown), and is fully connected between adjacent two-layer networks.Sigmoid typically use the Tanh function and the logistic func

1.computer Vision CV is an important direction of deep learning, CV generally includes: image recognition, target detection, neural style conversion Traditional neural network problems exist: the image of the input dimension is larger, as shown, this causes the weight of the W dimension is larger, then he occupies a larger amount of memory, calculate W calculati

first, the initialization of Proper weight initialization can prevent gradients from exploding and disappearing. For Relu activation functions, weights can be initialized to: Also known as "he initialization". For Tanh activation functions, the weights are initialized to: Also known as "Xavier initialization". You can also use the following formula to initialize: In the above formula, L refers to the first layer of the neural

1. Reading The Recurrent neural Network (NN) is the most commonly used neural network structure in NLP (Natural language Processing), and the convolution neural network is similar in the field of image recognition. Before we i

http://mp.weixin.qq.com/s?__biz=MjM5ODkzMzMwMQ==mid=2650408190idx=1sn= f22adfb13fb14f8a220222355659913f1. How to understand the status of NLP: see some tips for the latest doctoral dissertationIt may be a shortcut to look at the current status of an area and see the latest doctoral dissertation. For example, there are children's shoes asked how to understand the State-of-the-art of NLP, in fact, Stanford, Berkeley, CMU, JHU and other schools recently selected doctoral theses, the field of mainst

│││├──styletransfer.md│││└── Summary_generation_sequences.md││├──rossini_barbe (2). mid││├── Rossini_barbe (3) .mid││├──rossini_barbe.mid││├──scripts│ ││├──build│││├──convert_midi_dir_to_note_ Sequences.py│││└──convert_midi_dir_to_note_sequences_test.py││ └──testdata││├──build││├──example_complex.mid│ │├──example.mid││ └──notesequences.tfrecord│├──music││├──eval_melodies.tfrecord│ │├──generate│││├──2016-07-16_224233_1.mid││ nbsp │├──2016-07-16_224233_2.mid│││└──2016-07-16_224233_3.mid│ │├──or

Through the previous theoretical study, as well as the analysis of the relationship between error and weight, derive the formula to practice doing a own neural network through Python3.5:Follow the python introduction in the book and introduce the Zeros () in the NumPy:Import= Numpy.zeros ([3,2= 1a[] = 2a[2,1] = 5print(a)The result is:[1.0.][0.2.][0.5.]You can use

. Build model (Generative): Learning about the federated distribution of the observed data, such as 2-d: P (x, y). Discriminant model: The conditional probability distribution P (y|x) is learned, that is, the distribution of non-observable variables under the premise of observing the variable x.In layman's terms, we want to generate new data by generating models to learn the distribution from the data. For example, learn from a large number of images, and then create a new photo.And

operation process. and tensor have the same API, and some APIs for backward (). It also contains gradients related to tensor.Nn. Module-Neural network modules. Convenient data encapsulation, the ability to move operations to the GPU, but also include some input and output things.Nn. Parameter-A variable (Variable) that is automatically registered as a parameter when any value is assigned to the module.Auto

rate can reach 97% +The above can be UFLDL on the implementation of CNN, the most important thing is to figure out each layer in each process needs to be done, I summarize in the article at the beginning of the table ~matlab give me a big feeling is the matrix of demension match, sometimes know the formula is what kind of, However, to consider the dimensions of the matrix, the two-dimensional match matrix can be multiplied or added, but the benefit is that you don't know how to write the code w