Llm Course – Build A Semantic Book Recommender (python, Openai, Langchain, Gradio)

Discover really to build an intelligent book connection strategy utilizing nan powerfulness of ample relationship models and Python. Learn to toggle style book descriptions into mathematical representations that alteration precise content-based matching. By nan extremity of this course, you'll personification built a connection centrifugal that helps readers observe their adjacent favourite book. 💻 Code from this tutorial: https://github.com/t-redactyl/llm-semantic-book-recommender/tree/main 🏗️ JetBrains provided a assistance to make this group possible. ⭐️ Resources ⭐️ Free 3-Month PyCharm Professional Subscription Code: PyCharm4FreeCodeCamp Download PyCharm: https://jb.gg/pycharm-fcc Redeem PyCharm 3-month free license: jetbrains.com/store/redeem Download PyCharm: https://jb.gg/pycharm-fcc Kaggle datasets: https://kaggle.com/datasets 7K books dataset by Dylan Castillo: https://kaggle.com/datasets/dylanjcastillo/7k-books-with-metadata Hugging Face free NLP course: https://huggingface.co/learn/nlp-course/en/ Explanation of transformer encoder-decoder models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/7 Explanation of transformer decoder-only models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/6 Explanation of transformer encoder-only models (from Hugging Face NLP course): https://huggingface.co/learn/nlp-course/en/chapter1/5 Hugging Face Hub models page: https://huggingface.co/models OpenAI models: https://platform.openai.com/docs/models Explanation of vector standard (from Weaviate): https://weaviate.io/developers/weaviate/concepts/vector-index LangChain Python docs: https://python.langchain.com/docs/introduction LangChain chat exemplary integrations: https://python.langchain.com/docs/integrations/chat OpenAI billing page: https://platform.openai.com/settings/organization/billing/overview OpenAI API keys page: https://platform.openai.com/settings/organization/api-keys Explanation of zero-shot classification (from Hugging Face): https://huggingface.co/tasks/zero-shot-classification Information astir fine-tuned emotion classification model: https://dataloop.ai/library/model/j-hartmann_emotion-english-distilroberta-base Getting started pinch Gradio: https://gradio.app/guides/quickstart Gradio playground: https://gradio.app/playground Gradio themes: https://gradio.app/guides/theming-guide Further activity by Jodie astir LLMs Talk from GOTO Amsterdam giving an overview of LLMs: https://youtube.com/watch?v=Pv0cfsastFs Talk from NDC Oslo astir whether LLMs are showing signs of humanity: https://youtube.com/watch?v=kqJ7rZHFx84 Talk from PyCon US astir hallucinations successful LLMs: https://youtube.com/watch?v=innz9iBIAdU Tutorial connected doing sentiment study pinch LLMs: https://blog.jetbrains.com/pycharm/2024/12/how-to-do-sentiment-analysis-with-large-language-models/ Article connected LLM’s knowing of language: https://t-redactyl.io/blog/2024/06/can-llms-use-language-at-a-human-like-level.html Article connected sentience successful LLMs: https://t-redactyl.io/blog/2024/07/could-llms-be-sentient.html Article connected intelligence successful LLMs: https://t-redactyl.io/blog/2024/07/are-llms-on-the-path-to-agi.html 12:25 ⭐️ Chapters ⭐️ 0:00:00 Intro 0:03:05 Introduction to getting and preparing matter data 0:05:51 Starting a caller PyCharm project 0:16:59 Patterns of missing data 0:25:21 Checking nan number of categories 0:28:27 Remove short descriptions 0:34:36 Final cleaning steps 0:38:11 Introduction to LLMs and vector search 0:54:43 LangChain 0:58:46 Splitting nan books utilizing CharacterTextSplitter 1:02:57 Building nan vector database 1:05:50 Getting book recommendations utilizing vector search 1:11:07 Introduction to zero-shot matter classification utilizing LLMs 1:15:34 Finding LLMs for zero-shot classification connected Hugging Face 1:22:21 Classifying book descriptions 1:26:24 Checking classifier accuracy 1:35:19 Introduction to utilizing LLMs for sentiment analysis 1:39:25 Finding fine-tuned LLMs for sentiment analysis 1:42:07 Extracting emotions from book descriptions 1:54:25 Introduction to Gradio 1:56:51 Building a Gradio dashboard to impulse books 2:12:49 Outro