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!self reflection

work notes

2019-12-12 - summary the experiences from HUS

2018-12-02

  • chat bot
  • Image caption

2018-

  • latest two years from 2017
  • check the review number 10W+
  • the size of the model
  • dell user review data
  • config, reviews

2018-06-01

  • query=attributes traing using MLP
  • softmax
  • sigmoid for the binary classification

2018-05-31

  • read the paper - "predicting latent structured intents from shopping queries" - MLP
  • RNN
  • LSTM
  • claw the review data - "small" subsets
  • run basic word2vec on TF

2018-05-30

  • word embedding

+ data normalization

  • lemmatization and word stem ?

+ document vectorization

  • count vectorizer and TF-IDF vectorizer
  • word2vec <- could care about the order (word context) -> semantic

-> we get the similar vectors for the words

+

2018-05-29

  • setup google cloud <http://cs231n.github.io/gce-tutorial/>
  • NN algorithms
  • the server: 10.237.4.253 raymond/raymond

meetup with Dr. Wang on 28/05/2018

  • about tensorflow [1]:

+ Setup the env about python and tensorflow

+ now a simple tensorflow could be run in locally -> see the github [4]

+ plan (todo):

  • run a basic mode like linear regression in tensorflow
  • then run word2vec in tensorflow, or try to run it on server
  • about word embedding [2][3]:

+ the basic applied machine learning knowledge: like loss functions, bag of words, features, bag of vectors

  • if there is something wrong, if we could know the principle/theory, we could know the reason and correct it quickly

+ plan (todo):

  • know more about ML, especially deep learning (like word embedding part) based on the reference 2 and 3
  • Others

+ the github for the code

+ the fixed meetting time

2018-05-24

  • setup the env on python and tensorflow on mac
  • TODO:
  • setup the env on windows

virtualenvwrapper -> export WORKON_HOME=$HOME/.env

  • For some project
cd dev.dplearning
  • create an env
mkvirtualenv tfenv -> including the Python executable files, and a copy of the pip lib
  • use the env
workon tfenv -> using the virtual env
pip install *
  • deactivate
deactivate -> deactivate the env
rmvirtualenv venv
pip freeze > requirements.txt
pip install -r requirements.txt

reference

  • [Pipenv & Virtual Environments](http://docs.python-guide.org/en/latest/dev/virtualenvs/#lower-level-virtualenv)