convolutional neural network tutorial

, including neural network structure, forward propagation, reverse propagation, gradient descent and so on. The second part explains the basic structure of convolutional neural network, including convolution, pooling and full connection. In particular, it focuses on the deta

large number of outstanding academics joining the deep neural network, especially the Bengio research group at the University of Montreal and the NG Research Group at Stanford University. From the analysis of the proposed model, an important contribution of the Bengio research group is to propose a deep learning network based on the self-encoder (auto-encoder).

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

Python and be familiar with NumPy. Since this review is about how to use Theano, you should first read Theano basic tutorial. Once you have done this, read our Getting Started chapter---it will introduce concept definitions, datasets, and methods to optimize the model using random gradient descent.A purely supervised learning algorithm can be read in the following order:Logistic regression-using Theano for something simpleMultilayer perceptron-introd

is to "share the rights" (weight sharing), which allows a group of neurons to use the same connection right, a strategy that plays an important role in convolutional neural networks (convolutional neural Networks, referred to as CNN). For a CNN network:CNN can train with BP algorithm, but in training, whether it is th

Original page: Visualizing parts of convolutional neural Networks using Keras and CatsTranslation: convolutional neural network Combat (Visualization section)--using Keras to identify cats It is well known, that convolutional

1.why Look in case study This week we'll talk about some typical CNN models, and by learning these we can deepen our understanding of CNN and possibly apply them in practical applications or get inspiration from them. 2.Classic Networks The LENET-5 model was presented by Professor Yann LeCun in 1998 and is the first convolutional neural network to be successfull

Preface body RNN from Scratch RNN using Theano RNN using Keras PostScript "From simplicity to complexity, and then to Jane." "Foreword Skip the nonsense and look directly at the text After a period of study, I have a preliminary understanding of the basic principles of RNN and implementation methods, here are listed in three different RNN implementation methods for reference. RNN principle in the Internet can find a lot, I do not say here, say it will not be better than those, here first recomm

, computer vision and other fields. The Neuro directory under the Aforge.net source code contains a neural network class library.Aforge.net Home: http://www.aforgenet.com/Aforge.net Code Download: http://code.google.com/p/aforge/The class diagram for the Aforge.neuro project is as follows:Figure 10. Class diagram of Aforge.neuro class libraryHere are a few of the basic classes in Figure 9:Abstract base clas

network class Library.Aforge.net home: http://www.aforgenet.com/Aforge.net Code download: http://code.google.com/p/aforge/The class diagram for the Aforge.neuro project is as Follows:Figure 10. Class diagram of Aforge.neuro class libraryHere are a few of the basic classes in Figure 9:Abstract base class for Neuron-neuronsAbstract base class of layer-layer, consisting of multiple neuronsAbstract base class of Netw