neural networks and deep learning coursera

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

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

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

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

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

,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

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.

the composition of a convolutional neural network Image classification can be considered to be given a test picture as input Iϵrwxhxc Iϵrwxhxc, the output of this picture belongs to which category. The parameter W is the width of the image, H is the height, C is the number of channels, and C = 3 in the color image, and C = 1 in the grayscale image. The total number of categories will be set, for example in a total of 1000 categories in the Imagenet c

background: Introduction of Hyper-parameter debugging and processing 1-super-parameter debugging Compared with the earlier one, we can use the grid-like numerical division to do the numerical traversal to obtain the optimal parameters. However, in the field of deep learning, we generally try to use random methods to make parameters.The grid-like parameters in the above figure can only be fixed within 5 val

Wang, Min, Baoyuan Liu, and Hassan Foroosh. "Factorized convolutional neural Networks." ArXiv preprint (2016). This paper focuses on the optimization of the convolution layer in the deep network, which has three unique features:-Can be trained directly . You do not need to train the original model first, then use the sparse, compressed bits and so on to compress.