neural lace

Learning Goals Understand the convolution operation Understand the pooling operation Remember the vocabulary used in convolutional neural network (padding, stride, filter, ...) Build a convolutional neural network for Image Multi-Class classification "Chinese Translation"Learning GoalsUnderstanding convolution OperationsUnderstanding pooling Operationsremember vocabulary used in co

First, the main method of neural network performance tuning the technique of data augmented image preprocessing network initialization training The selection of activation function different regularization methods from the perspective of data integration of multiple depth networks 1. Data augmentation The generalization ability of the model can be improved by increasing the amount of data without changing the image category.The data augmentation metho

Neural Network Lecture VideoWhat are the neuronts?Storing numbers, returning function values for functionsHow are they connected?a1+ a2+ a3+ A4 +......+ An represents the activation value of the first levelΩ1ω2 ..... Ω7ω8 represents the weight valueCalculates the weighted sum, marks the positive weight value as green, the negative weight value is marked red, the darker the color, the closer the representation is to 0.This assigns a positive value to t

keras.utils.data_utils import get_file Import NumPy as NP import random import sys class rnnkeras:def __init__ (self, Sentencelen, vector_size, Output_size, Hidde n_dim=100): # Assign Instance Variables Self.sentencelen = Sentencelen Self.vector_size = vector_s ize self.output_size = output_size Self.hidden_dim = Hidden_dim self.__model_build__ () def _ _model_build__ (self): Self.model = sequential () Self.model.add lstm (Self.output_size, input_shape= (self.se Ntencelen, Self.vector_size)) Se

Python implements simple neural network algorithms and python neural network algorithms Python implements simple neural network algorithms for your reference. The specific content is as follows: Python implements L2 Neural Networks Including the input layer and output layer import numpy as np #sigmoid function def non

I've been focusing on CNN implementations for a while, looking at Caffe's code and Convnet2 's code. At present, the content of the single-machine multi-card is more interested, so pay special attention to Convnet2 about MULTI-GPU support.where Cuda-convnet2 's project address is published in: Google Code:cuda-convnet2A more important paper on MULTI-GPU is: one weird trick for parallelizing convolutional neural NetworksThis article will also give an a

Part five The second model: convolutional neural NetworksDemonstrates the convolution operationLeNet-5-type convolutional neural network is the core of the great breakthrough in the field of computer vision recently. The convolution layer differs from the previous fully connected layer by using some techniques to avoid excessive number of parameters, but preserves the model's descriptive ability. These tips

Sample program download: http://files.cnblogs.com/gpcuster/ANN1.rarIf you have any questions, refer to the FAQ first.If you do not find a satisfactory answer, you can leave a message below :)1 IntroductionI still remember hearing from senior students about Ann (Artificial Neural Network) when I first came into contact with RoboCup two years ago. This is amazing, he can learn to solve some problems well. Just like our people, we can learn and learn new

Convolution neural Network (convolutional neural Network, CNN) is a feedforward neural network, which is widely used in computer vision and other fields. This article will briefly introduce its principles and analyze the examples provided by the TensorFlow official. I. Principle of work Convolution is a basic method in image processing. The convolution kernel is

1. Some basic symbols2.COST function================backpropagation algorithm=============1. To calculate something 2. Forward vector graph, but in order to calculate the bias, it is necessary to use the backward transfer algorithm 3. Backward transfer Algorithm 4. Small topic ======== ======backpropagation intuition==============1. Forward calculation is similar to backward calculation 2. Consider only one example, cost function simplification 3. Theta =======implementation Note:unrolling param

LSTM unit.for the gradient explosion problem, it is usually a relatively simple strategy, such as Gradient clipping: in one iteration, the sum of the squares of each weighted gradient is greater than a certain threshold, and to avoid the weight matrix being updated too quickly, a scaling factor (the threshold divided by the sum of squares) is obtained, multiplying all the gradients by this factor. Resources:[1] The lecture notes on neural networks a

Here is the [1] derivation of the BP algorithm (backpropagation) steps to tidy up, memo Use. [1] the direct use of the matrix differential notation is deduced, the whole process is very concise. And there is a very big advantage of this matrix form is that it is very convenient to implement the programming Control.But its practical scalar calculation deduction also has certain advantages, for example, can clearly know that a weight is affected by who.Marking Conventions:$L $: The number of layer

Two types of classification: binary Multi-ClassThe following are two types of classification problems (one is binary classification, one is Multi-Class classification)If it is a binary classification classification problem, then the output layer has only one node (1 output unit, SL =1), hθ (x) is a real number,k=1 (K represents the node number in the output layer).Multi-Class Classification (with K categories): hθ (x) is a k-dimensional vector, SL =k, generally k>=3 (because if there are two cl

Artificial intelligence is not mysterious, will be a little subtraction enough. For neurons, when nerves are stimulated, the neurotransmitter is released to the next neuron, and the amount of neurotransmitters released by the next neuron is different for different levels of stimulation, so mimic this process to build a neural network: When entering a data x, simulate input an outside stimulus, after processing, the output of the result is f (x), the F

The neural network can be seen in two ways, one is the set of layers, the array of layers, and the other is the set of neurons, which is the graph composed of neuron.In a neuron-based implementation, you need to define two classes of Neuron, WeightAn instance of the neuron class is equivalent to a vertex,weight consisting of a linked list equivalent to an adjacency table and a inverse adjacency table.In the layer-based implementation, each layer corre

gradient descent algorithm to a normalized neural networkThe partial derivative of the normalized loss function is obtained:You can see the paranoid gradient drop. Learning rules do not change:And the weight of learning rules has become:This is the same as normal gradient descent learning rules, which adds a factor to readjust the weight of W. This adjustment is sometimes called weight decay .Then, the normalized learning rule for the weight of the r

+ b.tC. C = a.t + bD. C = a.t + b.t9. Please consider the following code: C results? (If you are unsure, run this lookup in Python at any time). AA = Np.random.randn (3, 3= NP.RANDOM.RANDN (3, 1= a*bA. This will trigger the broadcast mechanism, so B is copied three times, becomes (3,3), * represents the matrix corresponding element multiplied, so the size of C will be (3, 3)B. This will trigger the broadcast mechanism, so B is duplicated three times, becomes (3, 3), * represents matrix multipli

/***********************************************************************/ /* File: Mc_neuron.h * * 2014-06-04 //////* Description: Single-layer perceptron neural network header file */ /************************************************ / #ifndef _afx_mc_neuron_include_h_ #define _AFX_MC_NEURON_INCLUDE_H_ Class Neuron {public : Neuron (); Public: bool Tra

Recently in the study of Artificial neural network (Artificial neural netwroks), make notes, organize ideas Discrete single output perceptron algorithm, the legendary MP Two-valued Network: The value of the independent variable and its function, the value of the vector component only takes 0 and 1 functions, vectors Weight vector: w= (W1,W2,W3.....WN) Input vector: x= (X1,X2,X3.....XN) Training Sample

1 Introduction Remember when I first contacted RoboCup 2 years ago, I heard from my seniors that Ann (artificial neural network), this thing can be magical, he can learn to do some problems well enough to deal with. Just like us, we can learn new knowledge by studying. But for 2 years, I've always wanted to learn about Ann, but I haven't been successful. The main reason for this is that the introduction of this technology in our domestic tutorials i