udacity tensorflow

In the summary of Bayesian individualized sequencing (BPR) algorithm, we discuss the principle of Bayesian personalized sequencing (Bayesian personalized Ranking, hereinafter referred to as BPR), and we will use BPR to make a simple recommendation from the practical point of view. Since the existing mainstream open source class library has no BPR, and it is relatively simple, so with TensorFlow to implement a simple BPR algorithm, let us begin.1. BPR

Software Version Window10 X64 Python 3.6.4 (64-bit) CUDA CUDA Toolkit 9.0 (Sept 2017) CuDNN CuDNN v7.0.5 (Dec 5), for CUDA 9.0 The above version of the test passed.Installation steps:1. to install python, remember to tick pip. 2. detects if CUDA is supported .For more information on the NVIDIA website, see: Https://developer.nvidia.com/cuda-gpus, you can see if you can use

1. Preparation Windows 10 system, 3.6GHZ CPU, 16G memory Visual Studio or 2015 Download and install Git Download and install CMake Download Install Swigwin If you do not need Python bindings, you can skip Clone TensorFlow Switch TensorFlow to the git tag you want to compile Modify Tensorflow/contrib/cmake/cmakelists.txtif(Tensorflow_optimize_for

First, you can install a anaconda. You can then use the Python pip to install a specific version of the TensorFlow, such as Pip Install tensorflow-gpu==1.1.0 Upgrade to the latest: GPU Version: Pip Install--upgrade Tensorflow-gpu CPU Version: Pip Install--upgrade TensorFlow ============== How to view the curr

Copyright NOTICE: This article for Bo Master hjimce original article, the original address is http://blog.csdn.net/hjimce/article/details/51899683. I. Course of study Personal feeling for any deep learning library, such as Mxnet, TensorFlow, Theano, Caffe, and so on, basically I use the same learning process, the general process is as follows: (1) Training stage : Data Packaging-"network construction, training-" model preservation-"visual view of loss

Win7 Installing the anaconda+tensorflow+ configuration PycharmMarch 31, 2017 10:52:17Hits: 24251First summarize oneself encounters the pit: (Look back to think actually installs very simple) The first pit: Anaconda must install version 4.2, cannot install version 4.3; Full of blood and tears.Because we need to install our own Python must be 3.5 before we can call TensorFlowBut the anaconda4.3 is python3.6 and cannot be called TensorFlowSecond

TensorFlow is one of the widely used libraries for implementing Machine learning and other algorithms involving large numb Er of mathematical operations. TensorFlow is developed by Google and it's one of the most popular Machine Learning libraries on GitHub. Google uses TensorFlow for implementing Machine learning in almost all applications. For example, if your

Platform Use Caffe C++/cuda Fast So so Comprehensive Cnn All Systems Medium TensorFlow C++/cuda/python Medium Good Medium Cnn/rnn Linux\osx Difficult MXNet C++/cuda Fast Good Comprehensive Cnn All Systems Medium Torch C/lua/cuda Fast Good Comprehensive Cnn/r

TensorFlow TensorFlow (Tengsanfo) is Google based on the development of the second generation of artificial intelligence learning system, its name comes from its own operating principles. Tensor (tensor) means n-dimensional arrays, flow (stream) means the computation based on data flow diagram, TensorFlow flows from one end of the flow graph to the other.

There are three methods for reading Tensorflow data (next_batch ), Tensorflow data can be read in three ways: Preloaded data: pre-load data Feeding: Python generates data and then feeds the data to the backend. Reading from file: read directly from the file What are the differences between the three read methods? First, we need to know how TensorFlow (TF)

1, after the installation is complete, open anaconda Prompt, create tensorflow virtual environmentIn the prompt, enter:>>> Conda create-n TensorFlow python=3.52. Enter TensorFlow environment, enter>>> Activate TensorFlowBefore the command line, you can see the Add (TensorFlow) before entering the promptBecame like this

TensorFlow installed under Windows for study purposes, if you want to do the technology, see the relevant blog: CentOS7 installation TensorFlow1 , installation Pytho3.5First go to the Anaconda website to download the Windows version of the software, here Select the v3.6 version.Https://www.continuum.io2 , after the installation is complete, open Anaconda PromptThen we enter a command to see the installable version of

Reprint Please specify link: http://www.cnblogs.com/SSSR/p/5630534.htmlExamples in Tflearn training VGG16 project: https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py has not been tested successfully.The next project is to use a model that has been trained by others to make predictions, and the test works very well.Github:https://github.com/ry/tensorflow-vgg16 This project has been tested successfully, the effect is very good

1 Learning Goals: Learn the basic TensorFlow concept Using classes in TensorFlow LinearRegressor and predicting the median house value of each city block based on a single input feature Estimating the accuracy of model predictions using RMS error (RMSE) Improve model accuracy by adjusting the model's hyper-parameters Note: Data is based on California State 1990 census data.2 settin

TensorFlow InstallationSystem CentOS 7.0 64-bit:Python version: (note TensorFlow currently only supports python2.7 version)Install PIPYum update-y yum install-y python python-devel epel-release.noarch python-pipInstalling TensorFlow with PIPPip Install HTTPS://STORAGE.GOOGLEAPIS.COM/TENSORFLOW/LINUX/CPU/