Applied Generative AI and Natural Language Processing

Explore natural language processing foundations through word embeddings, from one-hot encoding to glove, visualize word vectors, and build a simple sentiment model with pre-trained embeddings.

Word embeddings built with neural networks, including Word2Vec CBOW and Skip-gram, GloVe, BERT, and GPT, capture semantic and syntactic meaning while reducing dimensionality.

Learn to obtain a word's embedding with GloVe and compute distances to other words to return the top five closest words.

Explore GloVe word clusters by reducing 100-dimensional embeddings to two dimensions, map closest words to categories like algebra, music, and numbers, and visualize effective word embeddings.

Apply pre-trained networks from Hugging Face to common NLP subtasks using open source models, and explore the range of tasks available for practical use.

Explore how text classification assigns predefined categories to text, enabling tasks like spam detection, topic categorization, and sentiment analysis, with models producing positive or negative probabilities.

Explore zero shot text classification and how pre-trained models apply to unseen classes without retraining. See how candidate labels, attributes, word embeddings, and natural language inference enable class probabilities.

Use a hugging face zero-shot classification pipeline with bert-large trained on the Multinli dataset to classify texts into entailment, neutral, or contradiction, applying candidate labels like crime, fantasy, and history.

Explore how vector databases store high dimensional embeddings for fast querying and similarity analysis across unstructured text and image data, enabling NLP, image search, and real-time analytics.

Explore the full workflow of building a Bible vector database: prepare the underlying corpus as text, tokenize, set up the vector store, add tokenized data, and query for relevant documents.

Build a single one-liner to show multimodal query results using a helper function, querying texts and images with open clip and chroma, and visualize the results.

Survey the OpenAI API and its models, and learn to set up an API key. Discover how REST APIs and Python interfaces let you interact with OpenAI models.

Install OpenAI Python package, fetch the API key from an environment variable, instantiate a client, and create a GPT-3.5-turbo chat completion with a user message, reading the first choice's content.

Set up a Python chat bot in Visual Studio Code using chat.py, retrieve the API key from environment variables, create an OpenAI client, and test with GPT 3.5 turbo.

Learn how REST APIs enable apps to talk to web services, based on representational state transfer, sending get or post requests that return json via http or https endpoints.

Explore OpenAI's web interface and playground to configure system and user prompts, adjust model, temperature, token length, top_p, and repetition penalties, then monitor usage, cost, and billing settings.

Explore cost differences across generative AI models and note prices may change; GPT-3.5 turbo balances performance and price, while GPT-4 costs significantly more.

Craft clear coding instructions by specifying output format, complexity, and level of detail. Learn through examples that contrast vague prompts with precise Python tasks, including docstrings and inline documentation.

Learn how to define personas by separating system prompts from user prompts to guide model behavior, set context, and use delimiters in cmd and shell environments.

Explore advanced prompting techniques, including zero-shot prompting, train-of-thought, self-consistency, and ensemble approaches to improve coherence and avoid correlation-causation traps.

Explore chain-of-thought prompts to reveal intermediate steps and improve reasoning, using train-of-thought and zero-shot variants with few-shot exemplars. Understand the benefits, limitations, and trade-offs, including transparency, interpretability, and higher costs.

Design a system prompt and evaluation workflow to explain the key events and significance of the American Civil War. Assess breadth, depth, and impacts, then seed and revise outputs.

Explore self-feedback, where a model self-evaluates its answers, rates performance, and produces revised outputs through iterations, prompts, and feedback, guided by stop criteria, using an American Civil War example.

Explore self-critique workflows with ideation, critique, and resolution steps, where a language model analyzes ideas, selects the best one, and refines it into a final output.

Explore the retrieval augmented generation workflow using embeddings and a vector database to find top two relevant texts, then pass them with the user query to a large language model.

Set up a vector database for retrieval augmented generation with chroma db and a movies collection, run a sample query, and format semicolon-separated titles and descriptions for a language model.