This bachelor's thesis examines the capabilities of ChatGPT 4 in code generation across 19 programming languages. The study analyzed solution rates across three difficulty levels, types of errors encountered, and code quality in terms of runtime and memory efficiency through a quantitative experiment. A total of 188 programming problems were selected from the LeetCode platform, and ChatGPT 4 was given three attempts to produce a correct solution with feedback. ChatGPT 4 successfully solved 39.67% of all tasks, with success rates decreasing significantly as problem complexity increased. Notably, the model faced considerable challenges with hard problems across all languages. ChatGPT 4 demonstrated higher competence in widely used languages, likely due to a larger volume and higher quality of training data. The solution rates also revealed a preference for languages with low abstraction levels and static typing. For popular languages, the most frequent error was "Wrong Answer," whereas for less popular languages, compiler and runtime errors prevailed, suggesting frequent misunderstandings and confusion regarding the structural characteristics of these languages. The model exhibited above-average runtime efficiency in all programming languages, showing a tendency toward statically typed and low-abstraction languages. Memory efficiency results varied significantly, with above-average performance in 14 languages and below-average performance in five languages. A slight preference for low-abstraction languages and a leaning toward dynamically typed languages in terms of memory efficiency were observed. Future research should include a larger number of tasks, iterations, and less popular languages. Additionally, ChatGPT 4's abilities in code interpretation and summarization, debugging, and the development of complex, practical code could be analyzed further. ---- Diese Bachelorarbeit untersucht die F\"ahigkeiten von ChatGPT 4 zur Code-Generierung in 19 Programmiersprachen. Betrachtet wurden die L\"osungsraten zwischen drei Schwierigkeitsgraden, die aufgetretenen Fehlerarten und die Qualit\"at des Codes hinsichtlich der Laufzeit- und Speichereffizienz in einem quantitativen Experiment. Dabei wurden 188 Programmierprobleme der Plattform LeetCode entnommen, wobei ChatGPT 4 jeweils drei Versuche hatte, mittels Feedback eine korrekte L\"osung zu generieren. ChatGPT 4 l\"oste 39,67 % aller Aufgaben erfolgreich, wobei die Erfolgsrate mit zunehmendem Schwierigkeitsgrad deutlich abnahm und bei komplexen Problemen in allen Sprachen signifikante Schwierigkeiten auftraten. Das Modell zeigte eine h\"ohere Kompetenz in weit verbreiteten Sprachen, was wahrscheinlich auf eine gr\"o{\ss}ere Menge und h\"ohere Qualit\"at der Trainingsdaten zur\"uckzuf\"uhren ist. Bez\"uglich der L\"osungsraten zeigte das Modell zudem eine Pr\"aferenz f\"ur Sprachen mit niedrigem Abstraktionsniveau und statischer Typisierung. Bei Sprachen hoher Popularit\"at trat der Fehler Wrong Answer am h\"aufigsten auf, w\"ahrend bei weniger popul\"aren Sprachen Compiler- und Laufzeitfehler \"uberwogen, was auf h\"aufige Missverst\"andnisse und Verwechslungen bez\"uglich der spezifischen strukturellen Eigenschaften dieser Sprachen zur\"uckzuf\"uhren ist. ChatGPT 4 demonstrierte in allen Programmiersprachen eine \"uberdurchschnittliche Laufzeiteffizienz und tendierte diesbez\"uglich erneut zu statisch typisierten und niedrig abstrahierten Sprachen. Die Werte zur Speichereffizienz variierten erheblich, wobei in 14 Sprachen \"uberdurchschnittliche und in f\"unf Sprachen unterdurchschnittliche Werte erzielt wurden. Es zeigte sich diesbez\"uglich eine leichte Tendenz zugunsten von niedrig abstrahierten sowie eine Pr\"aferenz zu dynamisch typisierten Sprachen. Zuk\"unftige Forschung sollte eine h\"ohere Anzahl an Aufgaben, Iterationen und unpopul\"aren Sprachen einbeziehen. Dar\"uber hinaus k\"onnten die F\"ahigkeiten von ChatGPT 4 in der Code-Interpretation und -Zusammenfassung, im Debugging und in der Entwicklung komplexer, praxisbezogener Codes analysiert werden.

Gensim is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. If this feature list left you scratching your head, you can first read more about the Vector Space Model and unsupervised document analysis on Wikipedia. This software depends on NumPy and Scipy, two Python packages for scientific computing. You must have them installed prior to installing gensim.

Python is now the de facto language of choice for machine learning. Although it is easy to learn, you can find some helpful tips that will help you get started or improve your knowledge. This post will show you how to learn programming languages and how to get help. You can learn a language in many different ways, whether you are learning it from a natural language like English or coding languages like Python. Baby learns a language by mimicking and listening.

Python has become a defacto lingua franca for machine learning. It is not a difficult language to learn, but if you are not particularly familiar with the language, there are some tips that can help you learn faster or better. In this post, you will discover what is the right way to learn a programming language and how to get help. How to Learn Python for Machine Learning Photo by Federico Di Dio, some rights reserved. There are many ways to learn a language, same for natural languages like English, or programming language like Python.

Python is a general-purpose interpreted programming language that is used for web development, data analysis, and machine learning. Python programming is a perfect language for python enthusiasts to understand better. To help you understand concepts better. Here are the top 10 python programming books. Automating Boring Stuff with Python is a go-to book for all python lovers. Even though the title of the book sounds boring, the book is not at all so.

What is Python and why is it so popular? This is a commonly Googled question today, even as more people turn to software programming / software development as a career option. There are many coding languages available today. But Python leads the pack. What is the reason behind the increasing demand for programmers proficient in Python?

Natural Language Processing (NLP), a tech wizard, is the part of data science that teaches computers to comprehend human languages. It involves the analysis of data to extract meaningful insights. Of its many uses, the main ones include text mining, text classification, text and sentiment analysis, and speech generation and recognition. Today, we explore seven top Python NLP libraries. Using these libraries will enable one to build end-to-end NLP solutions -- from getting data for one's model to presenting the results. Additionally, one will learn about related concepts such as tokenisation, stemming, semantic reasoning and more.

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. It is high-level built-in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms and can be freely distributed. Python has been growing in popularity over the last few years.

Here's how we used the hundreds of thousands of publicly accessible repos on GitHub to learn more about the current state of data science. Inspired by research carried out 2 years ago by the Design Lab team at UC San Diego, the JetBrains Datalore team decided to download all Jupyter notebooks accessible in October 2019 and October 2020 to gather statistics on the tools that the global DS community has been using in recent years. By October 2020 this number had grown 8 times, and we were able to download 9,720,000 notebooks. We made this dataset publicly available, and you can find the instructions for accessing it at the bottom of the post. Feel free to play with it and share your insights with us by mentioning @JBDatalore on Twitter, or write to us at contact@datalore.jetbrains.com.

The book begins with the very foundations of OOP and then uses practical examples to show how to correctly implement Object Oriented Programming in Python. Many examples are taken from real-world projects. The book focuses on high-level design as well as the gritty details of the Python syntax. The provided exercises inspire the reader to think about his or her own code, rather than providing solved problems. If you're new to Object Oriented Programming techniques, or if you have basic Python skills and wish to learn in depth how and when to correctly apply Object Oriented Programming in Python, this is the book for you.