Learn TensorFlow

Reference

모두를 위한 머신러닝 / 딥러닝 강의 by Sung Kim (https://hunkim.github.io/ml/)

Goals

• Basic understanding of machine learning algorithms
  • Linear regression, Logistic regression(classification)
  • Neural networks, Convolutional Neural Network, Recurrent Neural Network
• Solve your problems using machine learning tools
  • Tensorflow and Python

Acknowledgement

• Andrew Ng's ML class(coursera)
  • https://class.coursera.org/ml-003/lecture
  • http://www.holehouse.org/mlclass/ (note)
• Convolutional Neural Networks for Visual Recognition
  • http://cs231n.github.io/
• Tensorflow
  • https://www.tensorflow.org
  • https://github.com/aymericdamien/TensorFlow-Examples

Schedule

• Machine learning basic concepts
• Linear regression
• Logistic regression (classification)
• Multivariable (Vector) linear/logistic regression
• Neural networks
• Deep learning
  • CNN
  • RNN
  • Bidirectional Neural networks

Installation

python 3.5.1 with pyenv, virtualenv, autoenv (https://github.com/yyuu/pyenv-virtualenv) (https://dobest.io/how-to-set-python-dev-env/)

$ pyenv activate learntf

Install TensorFlow

Mac OS X, CPU only, Python 3.4 or 3.5: (learntf)$ export TF_BINARY_URL=https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-0.12.1-py3-none-any.whl (learntf)$ pip install --upgrade $TF_BINARY_URL

Tools for Data Analysis

(https://jkeun.github.io/post/data-analysis-env-setting/) $ pip install jupyter $ pip install pandas $ pip install sklearn $ pip install matplotlib $ pip install seaborn $ pip install --upgrade --no-deps statsmodels