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SQL Query: 3 Methods for Calculating Cumulative SUM

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SQL provides various constructs for calculating cumulative sums, offering flexibility and efficiency in data analysis. In this article, we explore three distinct SQL queries that facilitate the computation of cumulative sums. Each query leverages different SQL constructs to achieve the desired outcome, catering to diverse analytical needs and preferences. Using Window Functions (e.g., PostgreSQL, SQL Server, Oracle) SELECT id, value, SUM(value) OVER (ORDER BY id) AS cumulative_sum  FROM your_table; This query uses the SUM() window function with the OVER clause to calculate the cumulative sum of the value column ordered by the id column. Using Subqueries (e.g., MySQL, SQLite): SELECT t1.id, t1.value, SUM(t2.value) AS cumulative_sum FROM your_table t1 JOIN your_table t2 ON t1.id >= t2.id GROUP BY t1.id, t1.value ORDER BY t1.id; This query uses a self-join to calculate the cumulative sum. It joins the table with itself, matching rows where the id in the first table is greater than or

The Growth of Machine Learning till TensorFlow

The Internet and the vast amount of data are inspirations for CEOs of big corporations to start to use Machine learning. It is to provide a better experience to users.

How TensorFlow Starts

Let us take Amazon, online retail that uses Machine learning. The algorithm's purpose is to generate revenue. Based on user search data, the ML application provides information or insights.

The other example is the advertising platform where Google is a leader in this line. Where it shows ads based on the user movements while surfing the web. These are just a few, but there are many in reality.

TensorFlow is a new generation framework for Machine Learning developers. Here is the flow of how it started.
Machine Learning


Evolution

Evolution of TensorFlow

Top ML Frameworks

Torch

  • The torch is the first framework developed in 2002 by Ronan Collobert. Initially, IBM and Facebook have shown much interest.
  • The interface language is Lua.
  • The primary focus is matrix calculations. It is suitable for developing neural networks.

Theano

  • It is developed in 2010 by the University of Montreal. It is highly reliable to process graphs (GPU).
  • Theano stores operations in a data structure called a graph, which it compiles into high-performance code. It uses Python routines.

Caffe

  • This framework is much popular in processing Image recognition.
  • Caffe is written in C++.
  • It is popular in Machine Learning and Neural networks.

Keras

  • It is well known for developing neural networks. 
  • The real advantages or simplicity and easy development.
  • Fran├žois Chollet created Keras as an interface to other machine learning frameworks, and many developers access Theano through Keras to combine Keras's simplicity with Theano's performance.

TensorFlow

This is developed by Google in 2015. You can use TensorFlow on Google cloud. It supports Python heavily. The core functions of this framework developed in .C++

Takeaways.

  1. The story of Machine Learning started in the 18th century.
  2. Python is the top interface language in the major ML frameworks.
  3. Python is the prime language you need for 20th-century Data science projects.

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