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README

System Requirements

  • Python 3.6.0

Setup

  • Download Miniconda from https://conda.io/miniconda.html
  • Install with bash Miniconda3-latest-MacOSX-x86_64.sh
  • Check Miniconda executable installed location which conda => /Users//miniconda3/bin/conda
  • Load pre-configured Python environment dependencies into Miniconda conda env create -f ./config/aind-environment-unix.yml
  • Alternatively if using pip, install dependencies as follows: pip install -r ./config/requirements.txt
  • Switch to Miniconda env source activate aind
  • Install PyGame for visualisations brew install sdl sdl_image sdl_mixer sdl_ttf portmidi mercurial pip install pygame

Run Apps

  • Sudoku (uses '.' for boxes where value not yet known) python ./term01/lesson01/function.py

  • Sudoku (uses '123456789' for boxes where value not yet known, and for each box with only one value possibility, for each of its peers it removes that value from the remaining list of possibilities python ./term01/lesson01/function2.py

  • Refer to separate Project 1 https://github.com/ltfschoen/AIND-Sudoku

  • Perceptron Step:

    cd ./term02/lesson02/perceptron_step; python perceptron_step.py

    alt tag

  • Logistic Regression (Gradient Descent Algorithm)

    cd ./term02/lesson02/logistic_regression; python logistic_regression.py

    alt tag

  • Neural Network XOR using Keras

    pip install keras
    pip install tensorflow
    cd ./term02/lesson02/part30_keras_neural_network_xor; python neural_network.py
    
  • Convolutional Neural Network (CNN) using Keras

    • Study Dimensionality of Convolutional Layer changes

       cd ./term02/lesson03; python conv-dims.py
       cd ./term02/lesson03; python conv-dims2.py
      
      • Study Max Pooling Layer
      cd ./term02/lesson03; python pool-dims.py
      
  • Recurrent Neural Network (RNN) using Keras

    • Plot Recursive Sequence

      cd ./term02/lesson04; python recursive_sequence.py
      

      alt tag

  • Natural Language Processing (NLP)

      ```
      cd ./term02/lesson07_nlp_intro; python count_words.py
      ```    
    

Run MyPy Lint (Static Type Checking)

  • Install MyPy python3 -m pip install mypy.
  • Install Typing python3 -m pip install typing.
  • Import Typing import typing; from typing import *,
  • Apply MyPy static type checking to an existing function and check that expected warnings occur def get_list_for_str(word: str) -> None:
  • Run MyPy Linter with mypy ./mypy/test.py
  • Apply MyPy with expected types so no warnings occur def get_list_for_str(word: str) -> typing.List[str]: