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A short introduction to some of the most known libraries in Python3 and R for data science. Here I intend to cover topics such as data manipulation, data cleaning, machine learning and databases in each language.
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Martin Brieger / Simple Convolutional Neural Network
GNU General Public License v3.0 or laterMy first neural network - built with Python, Scipy and Numpy
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Introduction to python packges for AI projects
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Jakob Blomquist / Corona Data Analysis
GNU General Public License v3.0 onlyA Jupyter notebook for analysis of corona cases
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Python course for programmers with a focus on features needed for machine learning
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This is a Collaborative-Based Product Recommendation Engine which recommends most correlated products to the Customer based on the Ratings patterns of other customers who bought that same product also.
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Oromion / Introduction2ScientificPython
GNU General Public License v2.0 or laterCourse called Introduction to Scientific Python. Please see the syllabus.
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Machine Learning Basics - implementation of: loss functions (cross entropy loss, L1 loss, L2 loss, hinge loss), regularizations (L1 regularizer, L2 regularizer, early stopping), gradient check, optimizers, training a simple deep model.
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