000 01481nam a22001817a 4500
999 _c46272
008 200704b ||||| |||| 00| 0 eng d
020 _a9781617294433
082 _a681.3.062 CHO/D
100 _aChollet,Francois
245 _aDeep learning with python
260 _aNewyork;
300 _a361p.
520 _aDeep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. It is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. In particular, Deep learning excels at solving machine perception problems: understanding the content of image data, video data, or sound data. Here's a simple example: say you have a large collection of images, and that you want tags associated with each image, for example, "dog," "cat," etc. Deep learning can allow you to create a system that understands how to map such tags to images, learning only from examples. This system can then be applied to new images, automating the task of photo tagging. A deep learning model only has to be fed examples of a task to start generating useful results on new data
650 _aPython-Computer Program Language;
650 _aMachine Learning;
650 _aNeural Networks-Computer Science;
942 _2udc