Applications of Large Language Models in Python
Sumit Jaiswal (~justjais) |
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Description:
Dive into the fascinating world of Large language models (LLM) and learn how to properly use Python for text data analysis applications. Discover how these models may enhance your projects and impact the data science community by generating human-like content, translating languages, analyzing sentiment, and summarizing text. The rapid advancement of artificial intelligence has led to the development of sophisticated language models that can understand, generate, and analyze human language. Python, as a versatile and widely-used programming language, has become an ideal platform for implementing LLMs. This presentation will delve into the various applications of LLMs in Python, including:
- Using LLMs for chatbots, virtual assistants, and conversational AI
- Text generation and summarization
- Sentiment analysis and emotion detection
- Machine translation and multilingual communication
- Content generation and enrichment
By the end of this presentation, attendees will have a solid understanding of how LLMs can be leveraged in Python to build innovative and intelligent applications. I will also provide practical examples and resources for further exploration and development. Please Join me for an adventure to delve deeper into this interesting topic and explore new avenues for creativity!
Prerequisites:
The pre-requisites for an audience to effectively check out a presentation on the topic are:
Basic understanding of Python: Familiarity with Python syntax, data structures, and object-oriented programming concepts will be helpful.
General familiarity with machine learning: Having a basic understanding of machine learning principles and techniques, including supervised learning, unsupervised learning, and neural networks, will help the audience appreciate the applications discussed in the presentation.
Knowledge of Python IDEs and text editors: The audience should be comfortable using Integrated Development Environments (IDEs) or text editors like Visual Studio Code, Jupyter Notebooks, or PyCharm for writing and editing Python code. Also, using the command line, and install software packages using tools like pip or conda.
Curiosity and enthusiasm: A genuine interest in language models, artificial intelligence, and Python programming will significantly enhance the audience's learning experience during and after the presentation.
By ensuring that the audience meets these pre-requisites, you can create an engaging and informative session on Applications of Large Language Models in Python. If any questions arise regarding these pre-requisites, please let me know.
Speaker Info:
With over a decade of experience weaving my way through the intricate web of technological advancements, I, Sumit Jaiswal, have journeyed from the depths of device drivers to the thrilling heights of AI and machine learning. I am a open-source enthusiast and enjoy experimenting with different open-source technologies. Currently residing in Noida, I've previously lived in Bangalore for more than a decade and fondly miss the city's weather and its Biryani. I am a adventurous soul, who loves traveling and engaging in discussions about emerging technologies throughout the day.
Speaker Links:
As, I am part of RedHat Ansible organization I actively contribute towards writing blog around Ansible and its applications, ref: https://www.ansible.com/authors/sumit-jaiswal/ I've delivered multiple talks at several conferences like AnsibleFest, DevConf, and Config Management Camp. All of the talks are available at their respective youtube channels. LinkedIn profile: https://www.linkedin.com/in/sumit-jaiswal-justjais-6a480467/