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ML-DL-implementation

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Machine Learning and Deep Learning library in python using numpy and matplotlib.

Why this repository?


This repository gives beginners and newcomers in the field of AI and ML a chance to understand the inner workings of popular learning algorithms by presenting them with a simple to analyze the implementation of ML and DL algorithms in pure python using only numpy as a backend for linear algebraic computations for the sake of efficiency.

The goal of this repository is not to create the most efficient implementation but the most transparent one, so that anyone with little knowledge of the field can contribute and learn.

Installation

You can install the library by running the following command,

python3 setup.py install

For development purposes, you can use the option develop as shown below,

python3 setup.py develop

Testing

For testing your patch locally follow the steps given below,

  1. Install pytest-cov. Skip this step if you are already having the package.
  2. Run, python3 -m pytest --doctest-modules --cov=./ --cov-report=html. Look for, htmlcov/index.html and open it in your browser, which will show the coverage report. Try to ensure that the coverage is not decreasing by more than 1% for your patch.

Contributing to the repository

Follow the following steps to get started with contributing to the repository.

  • Clone the project to you local environment. Use git clone https://github.com/RoboticsClubIITJ/ML-DL-implementation/ to get a local copy of the source code in your environment.

  • Install dependencies: You can use pip to install the dependendies on your computer. To install use pip install -r requirements.txt

  • Installation: use python setup.py develop if you want to setup for development or python setup.py install if you only want to try and test out the repository.

  • Make changes, work on a existing issue or create one. Once assigned you can start working on the issue.

  • While you are working please make sure you follow standard programming guidelines. When you send us a PR, your code will be checked for PEP8 formatting and soon some tests will be added so that your code does not break already existing code. Use tools like flake8 to check your code for correct formatting.

Algorithms Implemented

Algorithm Location Algorithm Location Algorithm Location
ACTIVATION FUNCTIONS OPTIMIZERS MODELS
Sigmoid activations.py Gradient Descent optimizers.py Linear Regression models.py
Tanh activations.py StochasticGradientDescent optimizers.py Logistic Regression models.py
Softmax activations.py Mini Batch Gradient Descent optimizers.py Decision Tree Classifier models.py
Softsign activations.py Momentum Gradient Descent optimizers.py KNN Classifier/Regessor models.py
Relu activations.py Nesterov Accelerated Descent optimizers.py Naive Bayes models.py
Leaky Relu activations.py Adagrad optimizers.py Gaussian Naive Bayes models.py
Elu activations.py Adadelta optimizers.py K Means Clustering models.py
Swish activations.py Adam optimizers.py Polynomial Regression models.py
Unit Step activations.py Bernoli Naive Bayes models.py
Multinomial Naive Bayes models.py
Principle component analysis models.py
Algorithm Location
LOSS FUNCTIONS
Mean Squared Error loss_func.py
Log Error loss_func.py
Absolute Error loss_func.py
Cosine Similarity loss_func.py

ml-dl-implementation's People

Contributors

0xnakul avatar abjcodes avatar agrawalshubham01 avatar devyani-code avatar goldexplosion avatar harsh-ux avatar hiteshidudeja avatar jhunterhobbs avatar kerinpithawala avatar kwanit1142 avatar parva-jain avatar player0109 avatar q-viper avatar ridhimakohli avatar rohansingh9001 avatar saisrichandra avatar saptashrungi avatar shreyasachan avatar shreyashukla2 avatar ssiddharth27 avatar taruntomar122 avatar tejas1510 avatar tusharnankani avatar udit-git-acc avatar utkarsh0702 avatar

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