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C extensions for pytorch","Neural Transfer with PyTorch","Creating extensions using numpy and scipy","Transfering a model from PyTorch to Caffe2 and Mobile using ONNX","Autograd: automatic differentiation","Training a classifier","Optional: Data Parallelism","Neural Networks","What is PyTorch?","Data Loading and Processing Tutorial","Deep Learning with PyTorch: A 60 Minute Blitz","Deep Learning for NLP with Pytorch","TensorFlow: Static Graphs","PyTorch: Variables and autograd","PyTorch: Defining new autograd functions","PyTorch: Control Flow + Weight Sharing","PyTorch: Custom nn Modules","PyTorch: nn","PyTorch: optim","Warm-up: numpy","PyTorch: Tensors","Autograd","nn package","Multi-GPU examples","Tensors","PyTorch for former Torch users","Advanced: Making Dynamic Decisions and the Bi-LSTM CRF","Deep Learning with PyTorch","Introduction to PyTorch","Sequence Models and Long-Short Term Memory Networks","Word Embeddings: Encoding Lexical Semantics","Learning PyTorch with Examples","Transfer Learning tutorial","Welcome to PyTorch Tutorials","Classifying Names with a Character-Level RNN","Generating Names with a Character-Level RNN","Writing Distributed Applications with PyTorch","Reinforcement Learning (DQN) tutorial","Translation with a Sequence to Sequence Network and Attention","Spatial Transformer Networks Tutorial"],titleterms:{"case":24,"class":9,"function":[5,7,14,22,26,27,31],"import":6,"long":29,"new":[14,26,31],"short":29,"static":[12,26,31],The:38,about:5,advanc:[26,33,36],affin:27,afterword:9,algorithm:37,allreduc:36,applic:36,arrai:[8,24],attent:38,augment:29,autograd:[4,13,14,21,31],automat:[4,28],backend:36,backprop:7,backward:22,bag:[27,30],beginn:33,blitz:10,block:27,bridg:[8,24],build:27,caffe2:3,camel:24,charact:[29,34,35],cifar10:5,classifi:[5,27,34],code:0,collect:36,commun:36,compon:27,compos:9,comput:[28,30],condit:26,content:[1,31],continu:30,control:[15,31],convert:[8,24],convnet:[22,32],convolut:5,cpu:23,creat:[2,6,27,28,34,35],crf:26,cuda:[1,8,24],custom:[0,16,31],data:[5,6,9,32,34,35,38,39],dataparallel:[6,23],dataset:[6,9],decis:26,decod:38,deep:[10,11,26,27],defin:[5,7,14,31],dens:30,depict:39,descent:1,devic:3,differenti:[4,28],discrimin:26,discuss:26,displai:1,distribut:36,doe:1,dqn:37,dummi:6,dynam:26,embed:30,encod:[30,38],evalu:[32,34,38],exampl:[2,22,23,27,29,30,31],exercis:[26,29,30,34,35,38],extens:[0,2],extract:37,extractor:32,featur:[29,32],few:32,field:26,file:38,finetun:32,fix:32,flow:[15,31],former:25,forward:22,from:3,gener:35,get:[8,30],gpu:[5,6,23],gradient:[1,4,21],gram:30,graph:[12,28,31],hook:22,how:1,hyperparamet:37,imag:[1,5,32],implement:[1,26],includ:0,index:24,initi:36,inplac:24,input:[1,34,37],intermedi:33,introduct:[1,28],iter:9,languag:30,learn:[10,11,26,27,31,32,37],less:2,level:[29,34,35],lexic:30,librari:28,linear:27,load:[1,5,9,32,38,39],logist:27,loop:37,loss:[1,5,7,26,35],lstm:[26,29],make:26,map:27,memori:[29,37],method:36,minut:10,mobil:3,model:[3,6,23,29,30,32,38,39],modul:[16,31],multi:23,multipl:5,name:[34,35],net:22,network:[1,5,7,27,29,34,35,37,38,39],neural:[1,5,7],next:5,nlp:11,non:27,normal:5,note:26,numpi:[2,8,19,24,31],object:27,onnx:3,oper:[8,28],optim:[5,18,27,31],option:6,our:36,out:24,own:36,packag:[1,22],parallel:6,paramet:[2,6],parametr:2,part:[23,29],place:24,plot:[34,35,38],point:36,predict:32,prepar:[0,34,35,38],probabl:27,process:9,python:0,pytorch:[0,1,3,8,10,11,13,14,15,16,17,18,20,25,27,28,29,30,31,33,36],random:26,recurr:22,regress:27,reinforc:37,replai:37,reshap:28,result:[6,34,38,39],ring:36,rnn:[34,35],run:[3,6,34],sampl:35,scipi:2,semant:30,seq2seq:38,sequenc:[29,38],setup:36,share:[15,31],simpl:[6,38],softmax:27,spatial:39,speech:29,srresnet:3,start:8,step:0,stn:39,style:1,summari:6,tabl:31,tag:[26,29],tagger:29,tensor:[8,20,24,28,31,34],tensorflow:[12,31],term:29,test:5,through:9,toolkit:26,topic:36,torch:[8,24,25,28],torchvis:9,train:[5,27,32,34,35,36,37,38,39],transfer:[1,3,32],transform:[9,39],translat:38,turn:34,tutori:[9,32,33,37,39],updat:7,user:[25,34],using:[2,3],util:37,variabl:[4,13,21,31],versu:26,visual:[32,38,39],warm:[19,31],weight:[7,15,31],welcom:33,what:[1,5,8],where:5,word:[27,30],work:1,write:36,your:0,zero:24}})