udemy deep learning

Deep Learning thesis notes (8) Latest deep learning Overview Zouxy09@qq.com Http://blog.csdn.net/zouxy09 I have read some papers at ordinary times, but I always feel that I will slowly forget it after reading it. I did not seem to have read it again one day. So I want to sum up some useful knowledge points in my thesi

theoretical knowledge : UFLDL data preprocessing and http://www.cnblogs.com/tornadomeet/archive/2013/04/20/3033149.htmlData preprocessing is a very important step in deep learning! If the acquisition of raw data is the most important step in deep learning, then the preprocessing of the raw data is an important part of

In the words of Russian MYC although is engaged in computer vision, but in school never contact neural network, let alone deep learning. When he was looking for a job, Deep learning was just beginning to get into people's eyes. But now if you are lucky enough to be interviewed by Myc, he will ask you this question

models on a variety of platforms, from mobile phones to individual cpu/gpu to hundreds of GPU cards distributed systems. From the current documentation, TensorFlow supports the CNN, RNN, and lstm algorithms, which are the most popular deep neural network models currently in Image,speech and NLP. This time Google open source depth learning system TensorFlow can be applied in many places, such as speech reco

Written before: busy, always in a walk stop, squeeze time, leave a chance to think. Intermittent, the study of deep learning also has a period of time, from the beginning of the small white to now is a primer, halfway to read a little article literature, there are many problems. The trip to Takayama has only just begun, and this series is designed to record the path and individual

Python vector: Import NumPy as np a = Np.array ([[[1,2],[3,4],[5,6]]) SUM0 = Np.sum (A, axis=0) sum1 = Np.sum (A, Axis=1) PR int SUM0 Print sum1 > Results: [9 12][3 7] Dropout In the training process of the deep Learning Network, for the Neural network unit, it is temporarily discarded from the network according to certain probability.Dropout is a big kill for CNN to prevent the effect of fitting. Output

9. Common models or methods of deep learning 9.1 autoencoder automatic Encoder One of the simplest ways of deep learning is to use the features of artificial neural networks. Artificial Neural Networks (ANN) itself are hierarchical systems. If a neural network is given, let's assume that the output is the same as the i

,callbacks=[checkpointer, History]) train () Personal experience: Feel Keras use is very convenient, at the same time the source code is very easy to read, we have to modify the algorithm, you can read the bottom of the source code, learning will not be like the bottom of the caffe so troublesome, personal feeling caffe the only advantage is that there are a lot of open model, the source code, , Keras is not the same, with Python,

This section mainly introduces a deep learning MATLAB version of the Toolbox, Deeplearntoolbox The code in the Toolbox is simple and feels more suitable for learning algorithms. There are common network structures, including deep networks (NN), sparse self-coding networks (SAE), CAE, depth belief networks (DBN) (based

Deep Learning Book recommendation, deep learning bookAI Bible Classic best-selling book in the field of deep learning! Has long ranked first in Amazon AI and machine learning boo

(DBN.RBM); Training for each layer of RBM Dbn.rbm{1} = Rbmtrain (Dbn.rbm{1}, X, opts); For i = 2:n x = Rbmup (Dbn.rbm{i-1}, x); Dbn.rbm{i} = Rbmtrain (Dbn.rbm{i}, X, opts); EndEndThe first thing to be greeted is the first layer of the Rbmtrain (), after each layer before train used Rbmup, Rbmup is actually a simple sentence Sigm (Repmat (RBM.C ', size (x, 1), 1) + x * RBM. W '); That is, the graph above is calculated from V to H, and the formula is Wx+cThe following a

Connect Because we want to learn the expression of features, we need to know more about features or hierarchical features. So before we talk about deep learning, we need to explain the features again (haha, we actually see such a good explanation of the features, but it is a pity that we don't put them here, so we are stuck here ). Iv. Features Features are the raw material of the machine

Transferred from: http://blog.csdn.net/zouxy09/article/details/8775518 Well, to this step, finally can talk to deep learning. Above we talk about why there are deep learning (let the machine automatically learn good features, and eliminate the manual selection process. As well as a hierarchical visual processing system

Deep Learning: Running CNN on iOS, deep learning ioscnn1 Introduction As an iOS developer, when studying deep learning, I always thought that I would run deep