keras deep learning tutorial

Transferred from: http://baojie.org/blog/2013/01/27/deep-learning-tutorials/A few good deep learning tutorials, with basic videos and speeches. Two articles and a comic book are attached. There are some additions later.Jeff Dean @ StanfordHttp://i.stanford.edu/infoseminar/dean.pdfAn introductory introduction to what DL

get started. David Silver has also recently published a short article on deep-enhanced learning. Deep Learning Framework : A lot of deep learning frameworks, the most famous three should be TensorFlow (Google), Torch (Facebo

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of machi

vector to the discriminator to discriminate the probability that the generator is generated by the hidden space vector. Use real, fake pictures with real/fake tags to train discriminator; To train generator, you can use the GAN model to lose the gradient of the generator weight. This means that in each step, the weight of the generator is moved to the direction that the discriminator is more likely to classify the image decoded by the generator as "true." In other words, you train the g

would be is implied on each input. The function would run after the image is resized and augmented. The function should take one argument:one image (Numpy tensor with rank 3), and should output a Numpy tensor with the SAM E shape. Data_format=none One of {"Channels_first", "Channels_last"}. "Channels_last" mode means that the images should has shape (samples, height, width, channels), "Channels_first" mode means that the images should has shape (samples, channels, height, width). It defaults to

1.1 machine learning basics-python deep machine learning, 1.1-python Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang Video tutorial: http://pan.baidu.com/s/1kVNe5EJ 1. course Introduction 2. Machine

First spit groove, deep learning development speed is really fast, deep learning framework is gradually iterative, it is really hard for me to engage in deep learning programmer. I began three years ago to learn

similar to the dimensionality reduction) method. Maximum pooling divides the input image into overlapping image matrix blocks, and each sub-region outputs its maximum value. The two reasons why the maximum pooling method is very effective in the visual processing problem are:(1) Reduce the computational complexity of the upper level by reducing the non-maximum value.(2) The result of pooling supports translation invariance. In the convolution layer, each pixel point has 8 orientations that can

many problems do not have an intuitive physical meaning), so they can achieve better results in large-scale training data.. In addition, from the perspective of Pattern Recognition features and classifiers, the deep learning framework combines feature and classifier into a framework and uses data to learn feature, this reduces the workload of manually designing feature (which is the most effort by engineer