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2023-05-13
A collection of resources relevant to the study of machine learning.
The k-means clustering algorithm converges to identify three groups within the data.
During my MSc. Data Science I regularly referred to the following two books:
Artificial Intelligence: A Modern Approach
An Introduction to Statistical Learning (ISLR)
ISLR's more verbose sibling, often referred to during my graduate data mining module as "the bible" of machine learning:
The Elements of Statistical Learning (ESL)
For deep learning, this book by Goodfellow et al. provides a thorough approach to its material - useful when in need of more detail:
Provides a faster method of computation than typical base Python approaches
The conventional way of handling datasets that fit on a single computer.
Provides reliable implementations of typical machine learning algorithms, alongside supporting functions for cross-validation and such.
The two most widely recognised methods of deep learning:
Part of TensorFlow is "Keras", a simpler approach to performing DL:
A great explanation of the intuition behind neural networks:
3Blue1Brown's "Neural networks" playlist (Youtube)
This online course by fast.ai is highly regarded:
This collaborative Github list, with 7.7k+ stars, contains a plethora of useful material:
awesome-artificial-intelligence (Github)
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