16 Hot Essential Cheat Sheets for Machine Learning and Deep Learning

If a worker wants to do something good, he must first sharpen his tools. In machine learning and deep learning research, excellent reference materials and manuals can often help us get twice the result with half the effort! Today I recommend a very popular project on GitHub

Numpy

Numpy, one of the core libraries of Python scientific computing, is capable of creating high-performance multidimensional array of array objects and providing tools for working with arrays.

Pandas

Pandas is a Numpy-based data analysis library that provides data structure and data analysis tools for the Python programming language

Scipy

Scipy is also a Numpy-based extension package that contains some mathematical algorithms and convenience equations in the Python language and is one of the core libraries of scientific computing.

Matplotlib

Matplotlib is Python's 2D drawing library that produces publishing quality level graphics in a variety of hard copy formats and cross-platform interactive environments.

SciPy-Linear Algebra

Various linear algebra calculation methods are performed using SciPy.

Scikit-Learn

Scikit-Learn (sketarn) is a library of machine learning algorithms implemented in Python. Sklearn can implement common machine learning algorithms such as data preprocessing, classification, regression, dimensionality reduction, and model selection.

TensorFlow

TensorFlow is one of the hottest deep learning frameworks that Google has developed today.

Keras

Keras is a high-level neural network API, Keras is written in pure Python and is based on Tensorflow, Theano, and CNTK backends.

Neural Network Cells

Neural Network Graphs

Neural Network Family

PySpark,

R Studio (dplyr and tidyr)

ggplot2

Jupyter Notebook

Dask