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Core Python Programming is a textbook on the Python programming language, written by Wesley J. Chun. The first edition of the book was released on December 14, 2000. [1] The second edition was released several years later on September 18, 2006. [2] Core Python Programming is mainly targeted at higher education students and IT professionals. [3]
David Jay Malan (/ m eɪ l ɛ n /) is an American computer scientist and professor. Malan is a Gordon McKay Professor of Computer Science at Harvard University, and is best known for teaching the course CS50, [2] [3] which is the largest open-learning course at Harvard University and Yale University and the largest massive open online course at EdX, with lectures being viewed by over a million ...
Python 3.0, released in 2008, was a major revision not completely backward-compatible with earlier versions. Python 2.7.18, released in 2020, was the last release of Python 2. [37] Python consistently ranks as one of the most popular programming languages, and has gained widespread use in the machine learning community. [38] [39] [40] [41]
At the 2002 FOSDEM conference in Brussels, Van Rossum received the 2001 Award for the Advancement of Free Software from the Free Software Foundation (FSF) for his work on Python. In May 2003, he received a NLUUG Award. [38] In 2006, he was recognized as a Distinguished Engineer by the Association for Computing Machinery.
Hausa Programming language that compiles to Python, for educational purposes. Hapy Github page: Hebrew: TamliLogo A Hebrew implementation of Logo. Genesis An esoteric programming language based on "Paleo-Hebrew". Hindi: OM Lang OM Lang is a multilingual, statically typed programming language that supports more than 9+ Indian languages.
kitty is a free and open-source GPU-accelerated [2] [3] terminal emulator for Linux, macOS, [4] and some BSD distributions. [5] Focused on performance and features, kitty is written in a mix of C and Python programming languages.
Federated learning (also known as collaborative learning) is a machine learning technique in a setting where multiple entities (often called clients) collaboratively train a model while keeping their data decentralized, [1] rather than centrally stored. A defining characteristic of federated learning is data heterogeneity.
Overview of the Probably Approximately Correct (PAC) Learning Framework. An introduction to the topic. L. Valiant. Probably Approximately Correct. Basic Books, 2013. In which Valiant argues that PAC learning describes how organisms evolve and learn. Littlestone, N.; Warmuth, M. K. (June 10, 1986). "Relating Data Compression and Learnability" (PDF).
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