💡 Top 6 Python Libraries for Machine Learning: arpatech.com/blog/top-6-pyt…

#MachineLearning #Python #Libraries Image
1. Numpy is a library that handles data, particularly one that helps us to manage large multidimensional arrays along with a huge collection of mathematical operations.
2. TensorFlow is an end-to-end python machine learning library to run numerical high-end computations.
3. Theano is a popular python library widely used to identify and detect different types of errors for unit-testing and self-verification
4. Keras is a leading open-source Python library written to build neural networks and projects of machine learning.
5. Pandas is an open-source Python package offering high-performance, easy-to-use data models and data analysis tools for Python programming for the labeled data.
6. Scipy works alongside NumPy arrays to offer a framework that delivers numerous mathematical approaches. It presents a complete stack of Linear Algebra functions used for more complex computations.

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More from @DataScienceDojo

Jan 4
👉 3 Tools to Track and Visualize the Execution of Your Python Code: kdnuggets.com/2021/12/3-tool…

#Python #VisualizationTools #DataScience
1.Loguru:Loguru is a library that aims to make logging in Python enjoyable. Loguru provides many interesting functionalities, but one functionality that is the most helpful is the ability to catch unexpected errors and display which value of a variable causes your code to fail.
2. snoop: snoop is a Python package that prints the lines of code being executed along with the values of each variable by adding only one decorator.
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Sep 7, 2021
💡 A comprehensive roadmap to data infrastructure: bvp.com/atlas/roadmap-…

#DataInfrastructure #Roadmap #DataScience
1. Growth in adoption of cloud software: As companies in all industries and sizes adopt various cloud-based software to run their businesses, they have had to deal with data sprawl across a number of different sources and systems.
2. Increase in the volume of accessible data: Movement to the cloud and the growth of software users around the world has also generated more data at an exponential rate.
Read 7 tweets

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