!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