100 Days of ML Coding
-
Updated
Jul 15, 2020
{{ message }}
100 Days of ML Coding
Accompanying source code for Machine Learning with TensorFlow. Refer to the book for step-by-step explanations.
Plain python implementations of basic machine learning algorithms
Collection of notebooks about quantitative finance, with interactive python code.
Python code for common Machine Learning Algorithms
Bare bone examples of machine learning in TensorFlow
General Assembly's 2015 Data Science course in Washington, DC
with video_demo.py about 20% speed compared to your 1.0 repo. but thanks much for sharing!
For extensive instructor led learning
gesture recognition toolkit
Simple machine learning library / 簡單易用的機器學習套件
Machine Learning Lectures at the European Space Agency (ESA) in 2018
Six snippets of code that made deep learning what it is today.
Pure Javascript manually written
Regression, Scrapers, and Visualization
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
Aulas da Escola de Inteligência Artificial de São Paulo
Today i add a license for this repository.
A blog which talks about machine learning, deep learning algorithms and the Math. and Machine learning algorithms written from scratch.
Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks.
Code for Java Deep Learning Cookbook
Starter code of Prof. Andrew Ng's machine learning MOOC in R statistical language
A simple machine learning framework written in Swift
Launch machine learning models into production using flask, docker etc.
Linear Prediction Model with Automated Feature Engineering and Selection Capabilities
Machine Learning Concepts with Concepts
MATLAB/Octave library for stochastic optimization algorithms: Version 1.0.17
Add a description, image, and links to the linear-regression topic page so that developers can more easily learn about it.
To associate your repository with the linear-regression topic, visit your repo's landing page and select "manage topics."
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)))