machine learning and neural networks

Bengio, LeCun, Jordan, Hinton, Schmidhuber, Ng, de Freitas and OpenAI had done Reddit AMA's. These is nice places-to-start to get a zeitgeist of the field.Hinton and Ng lectures at Coursera, UFLDL, cs224d and cs231n at Stanford, the deep learning course at udacity, and the sum Mer School at IPAM has excellent tutorials, video lectures and programming exercises that should help you get STARTED.NB Sp The online book by Nielsen, notes for cs231n, and blo

theoretical knowledge : Deep learning: 41 (Dropout simple understanding), in-depth learning (22) dropout shallow understanding and implementation, "improving neural networks by preventing Co-adaptation of feature detectors "Feel there is nothing to say, should be said in the citation of the two blog has been made very

Welcome reprint, Reprint Please specify: This article from Bin column Blog.csdn.net/xbinworld.Technical Exchange QQ Group: 433250724, Welcome to the algorithm, technology interested students to join.Recently, the next few posts will go back to the discussion of neural network structure, before I in "deep learning Method (V): convolutional Neural network CNN Class

How the reverse propagation algorithm works In the previous article, we saw how neural networks learn through gradient descent algorithms to change weights and biases. However, before we discussed how to calculate the gradient of the cost function, this is a great pity. In this article, we will introduce a fast computational gradient algorithm called reverse propagation.

) encoded= Dense (activation='Relu') (encoded) encoded= Dense (Ten, activation='Relu') (encoded) Encoder_output=Dense (Encoding_dim) (encoded)#Decoder Layersdecoded = dense (ten, activation='Relu') (encoder_output) decoded= Dense (activation='Relu') (decoded) decoded= Dense (+, activation='Relu') (decoded) decoded= Dense (784, activation='Tanh') (decoded)#construct the Autoencoder modelAutoencoder = Model (input=input_img, output=decoded)Next, use Model this module to build the model.The input i

value sharing (or weight reproduction) and time or spatial sub-sampling to obtain some degree of displacement, scale and deformation invariance.Question three:If the C1 layer is reduced to 4 feature plots, the same S2 is also reduced to 4 feature plots, with C3 and S4 corresponding to 11 feature graphs, then C3 and S2 connection conditionsQuestion Fourth:Full connection:C5 to the C4 layer convolution operation, the use of the full connection, that is, each C5 convolution core in S4 all 16 featu

Learning Goals Understand multiple foundational papers of convolutional neural networks Analyze the dimensionality reduction of a volume in a very deep network Understand and Implement a residual network Build a deep neural network using Keras Implement a skip-connection in your network Clo

This paper summarizes some contents from the 1th chapter of Neural Networks and deep learning.learning with gradient descent algorithm (learning with gradient descent)1. TargetWe want an algorithm that allows us to find weights and biases so that the output y (x) of the network can fit all the training input x.2. Price functions (cost function)Define a cost funct

(J,:)); End Outputoftest (:, m) = W2 ' *hiddentestoutput+b2;end%% result analysis% based on network output find out what kind of data belongs to m=1:400 Output_fore (m) =find (outputoftes T (:, M) ==max (Outputoftest (:, M))), END%BP Network prediction Error ERROR=OUTPUT_FORE-OUTPUT1 (n (1601:2000)) '; K=zeros (1,4); % find out which category of the inferred error belongs to which class for i=1:400 if error (i) ~=0 [B,c]=max (Testoutput (i,:)); Switch C Case 1 K (1) =k (1) +1

,x_test) Y_prediction_train = Predict (W,b,x_train) # # # # END CODE here # # # print train/test Errors print ("Train accuracy: {}% ". Format (100-np.mean (Np.abs (y_prediction_train-y_train) *)) print (" Test accuracy: {}% ". Format (100-np.me An (Np.abs (y_prediction_test-y_test)) *) d = {"Costs": costs, "y_prediction_test": Y_prediction_test, "Y_prediction_train": Y_prediction_train, "W": W, "B": B, "learning_rate": Learnin G_rate, "Num_iteRations ": num_iterations} print (d[" costs

Learning Goals: Understand the challenges of object Localization, Object Detection and Landmark finding Understand and implement Non-max suppression Understand and implement intersection over union Understand how we label a dataset for an object detection application Remember the vocabulary of object Detection (landmark, anchor, bounding box, grid, ...) "Chinese Translation"Learning