A collection of machine learning codes in python's numpy and tensorflow.
- k-means [19 Dec 2020]
- k-means with farthest-first heuristic (ffh) [11 Jan 2021]
- k-means++ [10 Jan 2021]
- Spectral Clustering
- Unnormalized Spectral Clustering [23 Dec 2020]
- PCA [9 Jan 2021]
- Classical MDS [22 Jan 2021]
- Fast ICA [23 Jan 2021]
- Logistic Regression [13 Jan 2021]
- l2 regularization [15 Jan 2021]
- Linear Regression [16 Jan 2021]
- Ridge Regression [18 Jan 2021]
- LASSO [20 Jan 2021]
- Bayesian Linear Regression [19 Jan 2021]
- Importance Sampling [21 Jan 2021]
Install requirements.txt file to make sure correct versions of libraries are being used.
- Python 3.8.x
- Jupyterlab==2.2.9
- Numpy==1.19.4
- Opencv-python==4.4.0.46
- Pandas==1.1.1
- Seaborn==0.11.0
- Scikit-learn==0.24.0
- Tensorflow==2.4.0
- Tensorflow-datasets==4.1.0
- Tensorflow-probability==0.11.1
- cvxpy==1.1.7
- Daumé III, Hal. "A course in machine learning." Publisher, ciml. info 5 (2012): 69.
- Bishop, Christopher M. Pattern recognition and machine learning. springer, 2006.
- Murphy, Kevin P. Machine learning: a probabilistic perspective. MIT press, 2012.
- Miller, Jeffrey W. mathematicalmonk youtube lecture study page Machine Learning Playlist.
The MIT License (MIT)
Copyright (c) 2020 Peratham Wiriyathammabhum