tensorflow for deep learning from linear regression to reinforcement learning

----------------------------------------------------------------------------------------- 5. Self-taught Learning Use the mnist data of 5-9 to train an autoencoder to obtain the W1 B1 parameter. Reshape W1: Use W1 B1 to extract features 0-4 Then, use softmax regression to train a classifier (a lazy autoencoder is stolen for only 200 iterations) Bytes ----------------------------------------------------

linear regression is based on the hypothesis of Gaussian distribution, and the Logistic regression is based on the hypothesis of Bernoulli distribution. If linear regression and Logistic regression cannot be understood from the p

" reflectOn the term "depth" in deep learning, people may think that deep learning can do more things than traditional machine learning algorithms, and it is a more "advanced" algorithm. And the fact may not be as we think, because from the point of view of the input and out

optimized to maximize the performance of their joint collaboration. The feature of the convolutional network model [9], which was adopted by Hinton in 2012 in the Imagenet competition, contains 60 million parameters learned from millions of samples. The features learned from Imagenet have a very strong generalization capability, which can be successfully applied to other datasets and tasks, such as object detection, tracking and retrieval. Another famous competition in the field of computer vis

be an initial model.And learning algorithm will fix it up according to the verification of its data. Therefore, PLA is a algorithm that gettingFinal hypothesis by several verifications.So we can get linear model by PLA.3. Linear RegressionWhat is linear regression? In fact,

0. OriginalDeep learning algorithms with applications to Video Analytics for A Smart city:a Survey1. Target DetectionThe goal of target detection is to pinpoint the location of the target in the image. Many work with deep learning algorithms has been proposed. We review the following representative work:SZEGEDY[28] modified the

Keywords:Gradient descent: It is to let the data along the direction of the maximum gradient, that is, the maximum function derivative drop down, so that it quickly close to the results.The formula for cost functions is too long to be played here. There are so many online.This non-linear regression is plainly a narrow version of the neural network.Python implementations:1 ImportNumPy as NP2 ImportRandom3 4

-level performance on Imagenet classification (Prelu) (2014) Faster r-cnn Towards Real-time object detection with region proposal Networks (2015) Fast r-cnn (2015) Spatial pyramid Pooling in deep convolutional networks for visual recognition (SPP Net) (2014) Generative adversarial Nets (2014) Spatial Transformer Networks (2015) Understanding deep image representations by inverting them (2015) .

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 onli

build the model.In the exponential distribution family expression of the Bernoulli distribution we have known:, thus obtained.Three assumptions for building a generalized linear model: Assuming that the Bernoulli distribution is met, , in Bernoulli distribution The derivation process is as follows:As with the least squares model, the next work is done by gradient descent or Newton's method.Note the above push to the result, rec

Neural network and support vector machine for deep learningIntroduction: Neural Networks (neural network) and support vector machines (SVM MACHINES,SVM) are the representative methods of statistical learning. It can be thought that neural networks and support vector machines both originate from the Perceptual machine (Perceptron). Perceptron is a linear classific

Tel-aviv University Deep Learning laboratory Ofir students wrote an article on how to get started in-depth study, translation, the benefit of biological information dog.Artificial neural networks have recently made breakthroughs in many areas, such as facial recognition, object discovery, and go, and deep learning has

CNN operation, the calculation is still very large, many of which are in fact repeated calculation; SVM model: And it is a linear model, it is obviously not the best choice when labeling data is not missing; Training test is divided into multiple steps: Regional nomination, feature extraction, classification, regression are disconnected training process, intermediate data also need to be saved sepa

per second (selective Search+fast r-cnn is 2~3s one). It is important to note that the latest version has combined the RPN network with the Fast R-CNN network-the proposal of RPN acquired directly to the ROI pooling layer, which is really the framework for using a CNN network for end-to-end target detection. Summary: Faster R-CNN has been separating the region proposal and CNN classifications together, using an end-to-end network for target detection, pre-acquisition of region proposal, and the

1.GAN Basic Idea Generation against network Gan (generative adversarial networks) is a generation model proposed by Goodfellow in 2014. The core idea of GAN comes from the Nash equilibrium of game theory. It is set to participate in the game as a generator (generator) and a discriminant (discriminator), the generator captures the potential distribution of real data samples and generates new data samples; The discriminant is a two classifier to determine whether the input is a real or a generate

Fitbit Date cal Step Dist floor sit inactive walk run1 January 1, 2014 2496 12803 9.14 15 1161 123 98 582 January 2, 2014 2513 12955 9.47 12 1188 112 67 733 January 3, 2014 2228 8768 6.31 16 1234 97 72 374 January 4, 2014 2336 8971 6.41 16 1140 174 113 135 January 5, 2014 2508 12078 9.05 8 1153 130 108 496 January 6, 2014 2763 15880 11.36 20 1061 177 141 617 January 7, 2014 2453 10895 7.78 12 1145 156 99 408 January 8, 2014 2449 11692 8.44 13 1164 121 118 37Attach (Fitbit) #可以直接用step表示fitbit $st

) In 2013, Nal Kalchbrenner and Phil Blunsom presented a new end-to-end encoder-decoder architecture for machine translation. In 2014, Sutskever developed a method called sequence-to-sequence (seq2seq) learning, and Google used this model to give a concrete implementation method in the tutorial of its deep learning framework