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Convolution and **Correlation** in Deep Learning, **Tensorflow** and Theano. Convolution in deep learning works by applying a kernel (a small matrix) to a larger input matrix. You slide this kernel on the input matrix from the top left to the bottom right. You perform element-wise multiplication on each slide (where the sliding distance is the stride. Of course, GPU version is faster, but CPU is easier to install and to configure. If you are using Anaconda installing **TensorFlow** can be done following these steps. First, you need to create a conda environment " **tensorflow** " by running the command: conda create -n **tensorflow** pip python=3.7. Where we discuss the meaning of an activation function in neural networks, discuss a few examples, and show a comparison of neural network training with different activation functions. The streaming_pearson_**correlation** function delegates to streaming_covariance the tracking of three [co]variances: The product-moment **correlation** ultimately returned is an idempotent. alexlee-gk / lpips- **tensorflow** Public. Notifications Fork 32; Star 110. **Tensorflow** port for the Learned Perceptual Image Patch Similarity (LPIPS) metric . License. BSD-2-Clause license 110 stars 32 forks Star Notifications. **Cross** **Correlation** of Multiple Random Signals? Question. 2 answers. Asked 25th Nov, 2019; ... How to iterate over indices in **tensorflow**? Question. 3 answers. Asked 27th Sep, 2019; Suganya Sakthivel;. https://github.com/stoerr/machinelearning-**tensorflow**/blob/master/published/CorrelationLossTest.ipynb. **Cross**-entropy is commonly used in machine learning as a loss function. **Cross**-entropy is a measure from the field of information theory, building upon entropy and generally calculating the difference between two probability distributions. It is closely related to but is different from KL divergence that calculates the relative entropy between two probability distributions, whereas **cross**-entropy. In mathematical optimization, statistics, machine learning and Deep Learning the Loss Function (also known as Cost Function or Error Function) is a function that defines a **correlation** between a series of values and a real number. That number represents conceptually the cost associated with an event or a set of values. Thanks for contributing an answer to **Cross** Validated! Please be sure to answer the question. Provide details and share your research! But avoid Asking for help, clarification, or responding to other answers. Making statements based on opinion; back them up with references or personal experience. Use MathJax to format equations. MathJax.