introduction

content

• deeplearning basic knowledge include nn,dnn,cnn,rnn.
• deeplearning practice application knowledge
• tensorflow 1.* basic knowledge
• hands on deeplearning project e.g. image/text classifier,imgstyletransfer,text2img,DCGAN
• many known model in deeplearning field
• advanced knowledge e.g. GAN AUTOML

repository content

• code for basic usage of tensorflow
• code for deeplearing project practice(by jupyter notebook)
• necessary project knowledge
• course knowledge notes and conclude

latest institution

• notes and important knowledge conclude
• project practice code

next step

• tf2.0 learning from mooc
• deeplearning theorical learning for advanced step
• more and more challengable project
• popular,known,or latest ML/DL/RL model implementation/review

env

• virtualenv is very facinating
• py3.6 tf1.12 other compatable packages

others

data source

• from mnist,flickr30k,housing and or so
• ignore adding to this repo,because of large bytes
• there are code and notes in this repo.
• necessary data can be fetched from web search