Christopher5106.github.io Website Review

an overview of de*p learning with code examples in python: convolutional networks, recurrent networks, residual networks, memory networks, generative adversarial networks, and rl networks.. course 1: learn to program de*p learning in pytorch, mxnet, cntk, tensorflow and keras!. object detection de*p learning frameworks for optical character recognition and docu*ent pretrained features. python dask: evaluate true skill of reinforcement learning agents with a distributed cluster of instances. ibm watson bluemix visual api : tutorial and visual accuracy of a custom clas*ifier. linear algebra for derivatives in multi-dimensional spaces : tensor products, inner and outer products... about loss functions, regularization and joint losses : multinomial logistic, cross entropy, square errors, euclidian, hinge, crammer and. element-research torch rnn tutorial for recurrent neural nets : let's predict time series with a laptop gpu. deploying with docker and kubernetes - tutorial from your pc to aws ec2, google cloud, microsoft azure or any private servers. supervised learning, unsupervised learning with spatial transformer networks tutorial in caffe and tensorflow : improve docu*ent. 4 ami to run the fastest cluster of gpu for scientific computing at minimal engineering cost thanks to ec2, spark, nvidia, bidmach. human intelligence versus artificial intelligence, de*p-learning for non-experts : where are we and what applications ?. computation power as you need with emr auto-terminating clusters: example for a random forest evaluation in python with 100 instances. the future paradise of programming thanks to aws lambda functions : let's send a newsletter for a jekyll github pages site with a lambda. image annotations : which file format and what features for an annotation tool?. html5 and ja*ascript: file upload with progress bar, client-side image resizing and multiple runtimes. create an object detector with opencv cascade clas*ifier : best practice and tutorial.