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95,98,101,103,124,126,130,131],access:[11,15,22,31,53,54,64,68,71,72,73,75,77,78,80,82,83,88,93,94,95,98,100,106,127,130,131,132],access_key_id:64,accident:78,accompani:22,accomplish:[53,78,103,113,127],accord:[11,25,26,28,29,31,33,35,36,38,50,51,53,61,62,97,108,113,115,132,136],accordingli:[18,19,20,26,28,33,38,53,64,69,72],account:[28,30,37,38,88,103],accumul:[25,27,28,30,34,37,38,53,56,61,69,73,97,132],accur:[28,30,37,38,108,115,125],accuraci:[9,17,24,25,31,63,68,69,72,77,94,97,101,102,108,111,115,118,121,122,124,128,132],achiev:[50,52,53,61,62,71,72,83,94,97,98,99,113,115,125,132,136],acm:[28,30,31,37,38],acquaint:2,acquir:[53,61],across:[13,19,26,33,36,46,50,51,52,62,69,71,72,82,94,98,112,115,127,136],act1:[5,7,25,44,45,47,52,63,82,115,124,128],act2:[25,45,63,82,115,124,128],act:[15,38,50,101,126],act_data:38,act_typ:[5,7,12,13,25,28,38,44,45,47,55,73,82,95,97,101,111,114,115,118,119,124,126,128,135],action:[15,23,38,51,53,94],activ:[5,7,12,13,15,18,19,25,26,28,31,33,36,38,44,45,47,52,53,54,55,57,58,73,82,83,88,95,97,101,102,103,105,107,108,111,113,114,115,118,119,122,124,126,128,135],activationacttyp:102,activationparam:73,actor:[78,94],actual:[12,15,16,17,23,25,28,30,31,38,51,53,54,56,61,73,93,106,113,119,125],acycl:62,adadelta:31,adagrad:[28,30,31,37,38,131],adam:[28,30,31,37,38,55,106,127,131,135],adam_upd:[28,30,31,37,38],adamax:31,adapt:[28,31,38,82,99,111],add:[2,7,11,15,17,18,19,20,22,24,25,26,27,28,30,31,32,33,34,36,37,38,44,47,50,51,53,55,57,61,62,63,73,74,83,86,88,89,92,93,95,96,101,103,105,108,109,111,113,118,121,126,127,129,132,135],add_argu:[61,73],add_cflag:[74,89],add_enum:73,add_exec:132,add_filelin:61,add_ldflag:89,add_n:[28,30,37,38],add_subplot:135,added:[15,18,22,23,24,25,28,36,38,55,60,61,73,93,135],addhandl:103,adding:[23,25,31,42,50,52,54,57,61,62,64,67,76,88,89,103,132,135],addit:[2,3,6,11,12,15,16,19,22,23,24,25,26,28,31,33,38,39,46,48,52,54,61,62,63,69,77,79,82,86,88,94,98,109,113,116,125,130,132],addition:[49,78,101],address:[28,38,49,50,52,59,70,71,75,108,115,127,129],addscalarlay:54,adj:[28,38],adjac:[28,38,62,99],adjust:[28,38],adopt:[6,28,38,52,55,67],adv:92,advanc:[12,28,50,52,54,56,83,108,109,110,115,122,135],advantag:[7,38,44,47,50,51,52,54,82,83,94,98,101,108,115,131],adversari:[8,78,110],advoc:54,affect:[8,20,28,29,35,46,66,75],affin:[28,38,108,115],affine_matrix:[28,38],affirm:127,afford:[52,82,127],aforement:61,africa:127,after:[2,7,9,11,13,15,17,18,19,20,22,23,25,26,28,30,33,36,38,39,42,44,47,48,50,51,52,53,55,57,61,66,68,69,71,75,76,77,79,82,88,89,92,93,97,99,102,103,104,107,108,111,115,125,127,133,135,136],after_1:[28,38],after_2:[28,38],after_n:[28,38],afterward:[25,28,38],again:[3,8,22,51,61,68,108,115,122],against:[26,33,86,113,127,128,132,135,136],agent:[103,136],aggreg:[11,51,52,69,72,106,130],agnost:[7,38,44,47],ago:88,agre:71,ahead:115,aid:53,ail:127,aim:[25,50,51,66,74,108,115],aka:[22,26,28,33,38],akiaiosfodnn7exampl:64,alert:103,alex:[28,31,38],alexnet:[15,69,74,75,136],algebra:[21,64,82,86,88,89,108,115],algo:[28,38],algorithm:[11,26,28,33,38,42,50,51,61,62,66,68,74,82,83,94,108,113,114,115,121,130,135],alia:[16,17,22,28,31,38,86],align:[11,50,133],alik:101,aliv:103,all:[2,4,7,11,12,13,14,15,16,17,18,19,20,22,23,24,25,26,27,28,29,30,31,33,34,35,36,37,38,42,43,45,46,47,51,52,53,54,55,56,57,61,62,64,66,68,69,71,72,73,74,75,76,79,82,83,86,88,92,93,95,96,97,98,99,100,101,102,103,105,107,108,111,112,115,116,117,121,125,126,127,129,130,131,132,135,136],all_lay:[68,116],alloc:[8,12,15,20,25,32,38,39,45,48,51,53,54,57,61,66,82,97,98,101,103,105,126,132],allow:[2,8,11,12,14,15,18,19,21,24,25,26,28,30,32,33,36,38,42,46,50,51,53,54,56,57,61,62,64,67,76,77,78,82,88,95,98,101,103,104,111,115,118,125,130,135],allow_deferred_init:15,allow_extra:25,allow_extra_output:24,allow_extra_param:[12,67],allow_miss:[15,25,68,116],allow_up_s:12,almost:[6,28,52,53,88,101,102,124],almost_equ:38,alon:[32,50,68],along:[15,16,22,26,28,29,30,33,35,37,38,53,54,80,82,95,98,108,109,111,115,130,135],alongsid:53,alpha:[18,22,27,28,29,32,34,35,38,73],alphabet:[16,26,33,38],alphabet_s:[16,26,33],alphastd:20,alreadi:[4,15,23,25,42,43,46,50,52,54,55,57,67,68,86,88,93,112,113,124,128,130],also:[1,2,4,6,7,8,11,15,16,18,19,20,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,43,44,45,46,47,48,50,51,52,53,54,55,56,57,61,62,63,64,66,67,69,70,71,72,73,75,77,78,79,83,86,88,89,92,93,94,95,97,98,100,101,102,103,107,108,109,110,112,113,114,115,116,117,118,119,120,122,125,126,129,130,131,132,135,136],alter:[28,57],altern:[11,13,28,38,41,45,53,56,73,88],although:[8,37,38,39,48,51,56,62,64,69,71,122],alwai:[8,11,14,25,26,28,29,30,33,35,37,38,51,52,54,57,73,75,82,99,108,115,130],always_copi:28,always_output_list:25,amazon:[31,44,46,69,75,78,88,98,125],amazonaw:[66,86,88,89,93],amazonturk:94,amdahl:54,america:127,ami:[63,64,88,136],among:[46,53,54,62,71,72,82,86,102,122],amount:[28,31,38,50,54,62,64,77,88,103,130,136],anaconda:[64,88,93],analog:[39,48,82],analysi:[50,56,61,94,103],analyt:[28,38]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API","Module API","NDArray API","MXNet Scala Symbolic API","Symbolic Configuration and Execution in Pictures","MXNet Architecture","Designing Efficient Data Loaders for Deep Learning","Dependency Engine for Deep Learning","Optimizing Memory Consumption in Deep Learning","MXNet System Architecture","Deep Learning Programming Style","MXNet 0.9 (NNVM) Release Note","Survey of Existing Interfaces and Implementations","Contribute to MXNet","MXNet Community","Join MXNet Development Discussion","Powered By","A Beginner’s Guide to Implementing Operators in MXNet Backend","Bucketing in MXNet","How to | Convert from Caffe to MXNet","MXNet on the Cloud","Develop and Hack MXNet","Environment Variables","Frequently Asked Questions","Fine-tune with Pretrained Models","Gradient Compression","MXNet FAQ","Training with Multiple GPUs Using Model Parallelism","Run MXNet on Multiple CPU/GPUs with Data Parallelism","How to Create New Operators (Layers)","NNPACK for Multi-Core CPU Support in MXNet","Some Tips for Improving MXNet Performance","Create a Dataset Using RecordIO","Use data from S3 for training","MXNet Security Best Practices","Deep Learning in a Single File for Smart Devices","How to visualize Neural Networks as computation graph","<no title>","Why MXNet?","<no title>","Contents","<no title>","Build MXNet from Source","<no title>","Installing MXNet","Installing MXNet froum source on OS X (Mac)","<no title>","<no title>","Installing MXNet on Ubuntu","Installing MXNet in Windows","MXNet Model Zoo","Iterators - Loading data","Image IO - Loading and pre-processing images","Module - Neural network training and inference","NDArray - Imperative tensor operations on CPU/GPU","NDArray Indexing - Array indexing features","Record IO - Pack free-format data in binary files","Symbol - Neural network graphs and auto-differentiation","Basics","Real-time Object Detection with MXNet On The Raspberry Pi","Automatic differentiation","Creating custom operators with numpy","Gluon - Neural network building blocks","Hybrid - Faster training and easy deployment","Handwritten Digit Recognition","NDArray - Scientific computing on CPU and GPU","Tutorials","Text Classification Using a Convolutional Neural Network on MXNet","Distributed Key-Value Store","Linear Regression","Matrix Factorization","Handwritten Digit Recognition","Predict with pre-trained models","Callback Function","Custom Iterator Tutorial","Customized loss function","Char RNN Example","Classify Images with a PreTrained Model","Develop a Neural Network with MXNet in Five Minutes","R Tutorials","Handwritten Digits Classification Competition","NDArray: Vectorized Tensor Computations on CPUs and GPUs","Symbol and Automatic Differentiation","Developing a Character-level Language model","Handwritten Digit Recognition","Run MXNet Scala Examples Using the IntelliJ IDE","CSRNDArray - NDArray in Compressed Sparse Row Storage Format","RowSparseNDArray - NDArray for Sparse Gradient Updates","Train a Linear Regression Model with Sparse Symbols","Connectionist Temporal Classification","Autoencoders","Generative Adversarial Network (GAN)","Large Scale Image 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