Machine_Learning_Algorithms_from_Scratch and Deep_Learning_Algorithms_from_Scratch
These two repositories are complements, as one focuses on fundamental machine learning algorithms and the other on deep learning, allowing users to implement a broader spectrum of AI techniques in MATLAB and Python by using both.
About Machine_Learning_Algorithms_from_Scratch
milaan9/Machine_Learning_Algorithms_from_Scratch
This repository explores the variety of techniques and algorithms commonly used in machine learning and the implementation in MATLAB and PYTHON.
This project helps machine learning practitioners understand the inner workings of various machine learning algorithms. It provides practical implementations in MATLAB and Python, allowing users to see how common techniques like Decision Trees, Naive Bayes, and K-Means Clustering are built from the ground up. The output is a deeper conceptual understanding and runnable code examples.
About Deep_Learning_Algorithms_from_Scratch
milaan9/Deep_Learning_Algorithms_from_Scratch
This repository explores the variety of techniques and algorithms commonly used in deep learning and the implementation in MATLAB and PYTHON
This repository helps you understand and implement a variety of deep learning techniques from the ground up. It provides clear examples in MATLAB and Python, walking you through the core concepts of deep learning algorithms. It's ideal for students, researchers, or data scientists looking to deepen their foundational knowledge and practical skills in building neural networks.
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