DL-MakeitSimple

This repository would help to have a quick insight of different Machine and Deep Learning concepts. On Futher, it helps you to dive deep into the concepts.

Major advantange of this is that you can try straight away on the notebook by opening it on the Colab. It would give you a clear instructions with recent working libraries

Hope you would enjoy it. Any suggestions are Welcomed.

Efficient Python Programming Tips:

Neural Network Beignner - Keras
Neural Network Master - PyTorch
Natural Language Processing - Spacy Financial dataset - Quandl, Stocker Image dataset - OpenCV, Pillow

High Score Kernels Follows:

1. Understand the data
2. Feature engineering and feature extraction

Common Python Tricks:

• TQDM - Progress Bar
• Pandas - Dataframe
• Shap - Machine Learning Model Explanation
• pylint - Styling guide

Simple GUI

• Guietta - Simple GUI

Community:

Kaggle : https://www.kaggle.com/ganeshanvinothkumar DS Glossary : https://www.kaggle.com/shivamb/data-science-glossary-on-kaggle/notebook

Best Practices

1. Always have a Separate import section at the start of the code or Notebook.
2. Follow PEP8 -- Style Guide for Python Code

Platforms on the dice.

1. Machine Learning Toolbox https://amitness.com/toolbox/

Best Books

1. Data Science HandBook - https://jakevdp.github.io/PythonDataScienceHandbook/

Some Image dataset preprocessing steps

1. Erosion
2. Dilusion
3. Increasing the brightness or bluring the image. (cv.medianBlur, cv.cvtColor, cv.threshold)
4. OpenCV Image Filter methods