Introduction to Large Language Models (LLMs) In Python

Develop a document reading virtual assistant with llms, differentiate chatbots, conversational agents, and virtual assistants, and enable abstract generation, text summarization, answer generation, and semantic search.

Learn to access the OpenAI API by creating and managing API keys, logging in, and setting up a paid account with billing and usage limits.

Explore LangChain, a Python library that enables building LLM-powered applications with prompts, models, memory, indexes, and chains, wrappers for OpenAI and Hugging Face, and text splitting, embeddings, and vector stores.

Learn to extract text from a single or multiple PDFs in Colab using PDFx, install the package, load a PDF, and prepare the text for LLMs and NLP tasks.

Read text from multiple PDFs in Python using PyPDF2 and PdfReader. Iterate a pdf_dir, extract text from each page, and handle errors for robust PDF ingestion.

Learn a neater workflow for reading and extracting text from multiple pdfs using a pdf reader, directory iteration, and a reusable extract_pdf_text function, with simple formatting and robust file handling.

Present a rationale for a preliminary natural language processing analysis that helps a virtual reading assistant prioritize papers and generate summaries using LLMs in Python.

Use entity matching to surface PDFs with keywords mangrove and carbon, building an NLP workflow with spaCy, gensim, and LangChain to map entities to PDF sources and identify three papers.

Extract keywords from text chunks and PDFs by preprocessing text, removing stop words, and applying a tf-idf vectorizer to identify top keywords per chunk or file.

Learn how tf-idf measures word importance across pdfs by combining term frequency and inverse document frequency to extract significant keywords, highlighting themes while discussing stop words and customized removal.

Identify text similarity and quantify similarity using tfidf with cosine similarity and euclidean distance on cleaned documents. Explore sentence transformers and BERT for document embeddings to find most similar documents.

Learn a practical workflow to summarize PDFs in Python, using PyPDF2 and transformers to generate chunked summaries. Inspect methodologies, results, and figure references to understand summarization processes.

Set up the open ai key, import packages, and load a pdf summarization pipeline to generate chunked summaries using Langchain and large language models.

Explore how to use LangChain with OpenAI to load PDFs, create embeddings with Chroma, and build a memory-enabled QA system that answers questions from given text.

Configure environment with OpenAPI keys and data paths, load PDFs, split text into 1000 chunks without overlaps, build a Chroma vector store with OpenAI embeddings, and run Q&A with GPT-3.5-turbo-16k.

Learn to use LlamaIndex to connect LLMs with your data, build a vector store index from PDFs, and query documents with an efficient query engine.

Explore prompt engineering by designing prompts that define task definition, set context, use demonstrations, and control generation to guide ai models toward desired outputs.

Explore prompt engineering with LangChain to build text classification prompts for spam vs. ham, using OpenAI, train-test split, and prompt templates. Evaluate accuracy with an extraction chain and examples.

Explore Hugging Face as a platform to access and collaborate on large language models, computer vision, and multimodal tasks, with practical steps to create tokens and use the transformers API.

Provide a brief introduction to NumPy, highlighting its ndarray-based multi-dimensional arrays for data science, and show import conventions like np.array and np.eye.

Explore Pandas data structures, including series and data frames, create them from dictionaries and lists, and use loc and iloc for indexing while reading external data.

Learn to clean data with pandas in Python: read_csv, drop unused columns, and handle nulls with isnull and fillna, imputing age and fare by mean.

Explore data visualization as a visual context for data science, highlighting patterns, trends, and correlations through bar charts, pie charts, histograms, box plots, line charts, and scatter plots.

Explore how distributed computing frameworks coordinate parallel tasks across multiple nodes, handle faults, optimize resources, and scale with growing data using examples like Hadoop and Spark.

Learn to access a large language model using the Hugging Face hub, install required packages, and run the Falcon seven instruct model to generate text from prompts.

Explore how ai hallucinations amplify bias and discrimination, including bias magnification and synthetic discrimination, and adopt a layered approach with data curation, truth checking, bias detection, and human oversight.