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
