tensorflow for deep learning from linear regression to reinforcement learning

model and will build a deep convolution neural network for mnist through these steps. Downloading data sets The official website of the Mnist dataset is the Yann LeCun ' s website (http://yann.lecun.com/exdb/mnist/ ）。 You can download the dataset directly. It is recommended that Python crawler code be used to automatically download and install this dataset: https://tensorflow.googlesource.com/tensorflow/+

contrast between deep learning, wide depth learning and logistic regression, and put a good wide depth model on-line with the original base model for AB Experiment. From the results, the Wide-depth learning model has a better effect on LINE/LINE. The concrete conclusions ar

, the ascending dimension, the formation of non-linear machine learning polynomial, and the polynomial, but also can be expressed as a matrix vector, if the periodic function can be expressed by the Taylor Formula trigonometric functions, that is, the famous Fourier transform, so ultimately, polynomial convex function, optimization problem, and polynomial fitting in prediction; common fitting with logistic

found that the simple DNN model was not significantly improved for CTR. and the individual DNN model itself has some bottlenecks, for example, when the user itself is a non-active user, because the interaction between itself and item is relatively small, resulting in a very sparse eigenvector, and deep learning model in dealing with this situation may be excessive generalization, Causes the recommendation

handled in the same way as C4.5. To avoid overfitting (Overfitting), the cart decision tree needs pruning. The prediction process is also very simple, according to the resulting decision tree model, the extension of matching eigenvalues to the last leaf node is the category of prediction.When you create a regression tree, the values of the observations are contiguous, with no categorical labels, and only the values derived from the observed data crea

predicted is discrete, that is, one label, it is a classification problem. The learning process is shown in: Common Terms in the above learning process: datasets containing house area and price are calledTraining set training set;Input variable X (area in this example) isFeature features;The output predicted value Y (house price in this example) isTarget value target;The fitting curve, usually y = h (x

training, 1.2 million imagenet image data. GPU implementation that reads and writes directly from the GPU memory. The LRN (local response normalization) normalization layer.Enhanced convolutional layer functionality.Vggnet,karen Simonyan, Andrew zisserman "Very deep convolutional Networks for Large_scale Visual recognition"/HTTP// www.robots.ox.ac.uk/~vgg/research/very_deep/. 5 convolution group (8-16 layer), 2 layer full-attached layer image feature

Learning notes TF009: logarithm probability regression, learning notes tf009 The logistic function, also known as the sigmoid function, is a probability distribution function. Given a specific input, calculate the probability of output "success" and the probability of "Yes" to the reply question. Accept a single input. Multi-dimensional data or training set sampl

articles about deep learning in various fields. Since 2013, deep learning even has its own special meeting: International Conference on Learning Representations (ICLR). From the name of the Conference can also be seen, deep

Absrtact: This paper introduces linear regression, local weighted regression and ridge regression, and uses Python to make simple implementation.Prior to this, we have learned logistic regression and continue to look back today. First, the origin of the return: The return w

of a positive class is greater than 0.5, it is determined that it is a positive class, otherwise it is a negative class. In fact, the class probability of SVM is the distance from the sample to the boundary. This activity actually makes logistic regression dry. Therefore, LogisticRegression is a linear regression after the logistic equation is normalized. Okay.

Pattern Recognition field Application machine learning scene is very many, handwriting recognition is one of the most simple digital recognition is a multi-class classification problem, we take this multi-class classification problem to introduce Google's latest open source TensorFlow framework, The content behind the deep le