Text summarization using NLP

List of Computer Vision APIs

Computer Vision APIs Different computer vision tools and APIs are : Google CV Watson VR Amazon R Microsoft CV Clarif.ai Cloudsight Scale https://www.scaleapi.com/image-annotation Imagga vize.ai https://vize.ai/ http://www.recognize.im/ Moodstocks ( http://www.moodstocks.com/pricing/ ) * Kooaba ( http://www.kooaba.com/en/plans_a... ) * IQ Engines ( https://www.iqengines.com/pricing/ ) * LTU technologies ( http://www.ltutech.com/ ) Camfind - Image recognition back-end for the popular app CamFind. Take advantage of the leading image recognition platform through an easy to use web API. Recognize API | Mashape - Vufind Recognize is a real-time image recognition API for classification and monetization of photos and videos. Recognize uses object recognition to uncover meaning and metadata of photos and videos for contextual image commerce and advertising. Kooaba - Our cloud-based image recognition solutions mak...

Explore python Libraries - Numpy, Scipy, Matplotlib

Numpy Gives functions for working with multidimensional arrays. Numpy arrays have 1)Rank 2)Shape (given by dim of vector) Numpy arrays can be created like: a=np.array([1,2,3],[2,3,4]) #with initialize a=np.zeros(2,2) / np.ones(2,2) / np.random.random(4,4)

Cat Vs Dog Classification Using TensorFlow CNN

This code is copy of - https://www.kaggle.com/sentdex/full-classification-example-with-convnet/notebook - work. I have tried this code and executed successfully. Description: The data set (training and test) are taken from Kaggle Dog vs Cat competition. https://www.kaggle.com/c/dogs-vs-cats-redux-kernels-edition/data In this the training data is encoded into an array containing the feature of each image plus its corresponding label. Functions used for this are : label_img() and create_training_data() . Similarly preprocessing is done for test data set also. The test data set images are stored as numpy array with its corresponding IDs. Function used is :process_test_data Numpy is used for preprocessing the image data into arrays. Using Tensorflow '- tflearn a DNN model is created. Here it has six convolutional layers followed by maxpool layers . Activation function used is 'RELU'. Then One fully connected layer with drop out and then softmax regression is also ...

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