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linear-regression
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100 Days of ML Coding
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Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
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Plain python implementations of basic machine learning algorithms
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Collection of notebooks about quantitative finance, with interactive python code.
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Bare bone examples of machine learning in TensorFlow
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Python code for common Machine Learning Algorithms
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General Assembly's 2015 Data Science course in Washington, DC
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Open
Yolov3 slow?
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yuntai
commented
Jul 12, 2019
with video_demo.py about 20% speed compared to your 1.0 repo. but thanks much for sharing!
For extensive instructor led learning
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Simple machine learning library / 簡單易用的機器學習套件
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Machine Learning Lectures at the European Space Agency (ESA) in 2018
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Six snippets of code that made deep learning what it is today.
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Pure Javascript manually written 👌 implementation of BLAS, Many numerical software applications use BLAS computations, including Armadillo, LAPACK, LINPACK, GNU Octave, Mathematica, MATLAB, NumPy, R, and Julia.
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吴恩达机器学习coursera课程,学习代码(2017年秋) The Stanford Coursera course on MachineLearning with Andrew Ng
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Aulas da Escola de Inteligência Artificial de São Paulo
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A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
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A day to day plan for this challenge. Covers both theoritical and practical aspects
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Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
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Code for Java Deep Learning Cookbook
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Dec 23, 2019 - Java
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
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A simple machine learning framework written in Swift 🤖
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Aug 28, 2018 - Swift
Launch machine learning models into production using flask, docker etc.
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Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
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MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.17
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Nov 20, 2018 - Terra
Codes and Project for Machine Learning Course, Fall 2018, University of Tabriz
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Hi I would like to propose a better implementation for 'test_indices':
We can remove the unneeded np.array casting:
Cleaner/New:
test_indices = list(set(range(len(texts))) - set(train_indices))
Old:
test_indices = np.array(list(set(range(len(texts))) - set(train_indices)))