tensorflow for deep learning from linear regression to reinforcement learning

tutorial.Https://www.tensorflow.org/versions/r0.9/tutorials/index.htmlI wanted to start with imagenet, but it did not teach the model how to build, directly to a model file, loaded in. So do not go back and start with the simplest example. This is the mnist (handwriting recognition) tutorial. Mnist This is a thing, everyone Google.TensorFlow's official website gives two examples, simple examples, through the General machine learning algorithm to achi

Machine learning problems are classified into classification and Regression Problems.Regression is used to predict continuous values. Unlike classification, regression is used to predict discrete types. As to why this type of problem is called regression, it should be a convention, and you cannot explain it.For exampl

less than 1000 in the same game. WaveNets, CNNs, and attention mechanisms Google's Tacotron 2 text-to-speech system is impressive. This system is based on WaveNet and is also an automatic regression model deployed in Google Assistant and has been rapidly improved over the past year. Moving away from expensive and training long regression architectures is a larger trend. In the paper Attention is All you N

and a class. It just learns how to refactor or reproduce its input. Or, it just learns to get a feature that can be well represented in the input, and this feature can represent the most important of the original input signal. So, in order to implement the classification, we can add a classifier (such as publication regression, SVM, etc.) to the topmost coding layer of autoencoder, and then train through the standard multi-layered neural network supe

of epsilon items! If the epsilon value is too low, the data after the whitening will appear to be noisy; Conversely, if the epsilon value is too high, the albino data will be too blurry compared to the original data.Epsilon method of selection:A. Draw the eigenvalues of the data graphically; b. Select a characteristic value that is larger than most of the noise in the data to reflect the epsilon .2. How to adjust the epsilon specifically? I don't know, if I had a exercise, I'd be fine.2. When p

regression, in fact, the previous blog also mentioned, is a similar "undetermined factor" for the y=kx+b in the K and B process, This is also the core idea of deep learning. Note that the following cases are all run based on node. JS encoding.Here are the official examples:Import * asTf from ' @tensorflow/tfjs ';//De

Deep learning has a profound effect on computer science. It makes it possible for cutting-edge technology to research and develop products that are used by tens of millions of of people everyday.The study announced the launch of the second-generation machine learning System (TENSORFLOW), which has been strengthened for

Learning notes TF049: TensorFlow model storage and loading, queue threads, loading data, custom operations, tf049tensorflow Generate the checkpoint file (chekpoint file). The extension is. ckpt, And the tf. train. Saver object is generated by calling Saver. save. Contains weights and other program-Defined variables, excluding the graph structure. Another program needs to re-create the graphic structure to t

6th Chapter Logistic regression and maximum entropyModelLogistic regression (regression) is a classical classification method in statistical learning. Max Entropy isone criterion of probabilistic model learning is to generalize it to the classification problem to get the max

angular regression and lasso Lars Description: How to find which function is provided by which package: http://cran.rstudio.com/->task views->machine learning-> Search "keyword, such as Lars"The execution code is as followsinstall.packages("lars"#http://cran.rstudio.com/ ->TASK Views->Machine Learning-> search larslibrary#library#lm.ridge函数在ridge

Nine algorithms for machine learning---regressionTransferred from: http://blog.csdn.net/xiaohai1232/article/details/59551240Regression analysis is to quantify the size of the dependent variable affected by the independent variable, to establish a linear regression equation or a nonlinear regression equation, so as to p

The regression in the previous section is a global regression model that sets a model, whether linear or non-linear, and then fits the data to obtain parameters. In reality, some data is very complex, the model is almost invisible to the public, so it is a little inappropriate to build a global model. This section desc

also called the sigmoid function. The curve is as follows: Given the input variable X, the probability of Y = 1 is given based on the selected parameter H (X. The probability of Y = 0 is 1-h (x) 6.3 decision Boundary Reference video: 6-3-demo-boundary (15 min ).mkv The decision boundary is the boundary between prediction 1 and prediction 0. TheDemo-boundaryIs the line that separates the area where Y = 0 and where Y = 1. It is created by our hypothesis function.

**************************************Note: This blog series is for bloggers to learn the "machine learning" course notes from Professor Andrew Ng of Stanford University. Bloggers deeply learned the course, do not summarize is easy to forget, according to the course plus their own to do not understand the problem of the addition of this series of blogs. This blog series includes linear

learning algorithms which are widely used in image classification in the industry and knn,svm,bp neural networks. Gain deep learning experience. Explore Google's machine learning framework TensorFlow. Below is the detailed implementation details. First, System design In thi

It is mentioned in this series that using Python to start machine learning (3: Data fitting and generalized linear regression) mentions the regression algorithm for numerical prediction. The logical regression algorithm is essentially re