Generative AI: OpenAI API, Gemini, DeepSeek, and ChatGPT

Install the openai and tiktoken libraries in your chosen environment using pip, with a beginner-friendly walkthrough in an Anaconda setup.

Explore OpenAI API pricing, compare per 1000 vs per 1 million tokens, and learn cost implications across GPT-3.5 turbo and GPT-4 variants, including input vs output tokens and context windows.

Use tiktoken to estimate prompt tokens for the ChatGPT API, calculate costs in dollars per token, and respect the maximum sequence length with GPT-3.5 Turbo.

Explores reproducibility in generative AI using a seed parameter and system fingerprint to achieve mostly consistent outputs. Note that occasional differences may occur despite the same inputs.

Explore turning the ChatGPT API into a memory-enabled chatbot by managing a history of system, user, and assistant messages for persistent conversations.

Explore frequency and presence penalties passed to the create function like temperature to control repetition and topic diversity in ChatGPT, with values from -2 to 2 and practical testing.

Explore how large language models like ChatGPT and GPT-4 drive dramatic NLP gains across extractive and abstractive tasks such as question answering, text summarization, and information extraction.

Explore how LMS change the machine learning workflow by reducing data collection, labeling, and model training, and weigh cost versus performance against traditional models like Naive Bayes.

Explore China's deep seq models and the open source Deep Sea API, comparing pricing and performance with OpenAI, and learn to use chat and reasoning models in Python.

Explore prompt engineering strategies in generative ai, including unstructured to structured data, json mode, classic nlp tasks, prompt chaining, chain-of-thought prompting, tone adjustment, and sentiment analysis.

Explore translation, language detection, and tone enhancement using ChatGPT and LMS, with practical prompts for multilingual customer service and stylistic tweaks like nerdy scientist or pirate styles.

Learn how to perform text summarization with ChatGPT using prompts, max tokens, and explain-like-I'm-five variants. Explore practical examples, including stem cell articles, for concise, readable summaries.

Explore semantic search by converting text, images, and tabular data into vectors, then use nearest neighbor search in embedding space to retrieve relevant documents and power recommendations and chatbots.

Explore OpenAI's embeddings endpoint and learn to convert text into vectors by passing a list of strings with text embedding three small model, returning a list of floats to arrays.

Leverage the embeddings API for retrieval-augmented generation, converting documents to vectors, storing them in a vector database, and using retrieved text as ChatGPT context.

Index normalized embeddings with faiss, ensure correct dimensionality, and retrieve context to feed a qa prompt that answers questions from the most relevant articles.

Learn what fine tuning is, when to use it for ChatGPT, and compare its pros and cons with prompt engineering and Rag, including hyperparameters and costs.

Learn to fine-tune ChatGPT by preparing a dataset in JSON, loading it from a repo, and converting reviews and responses into the OpenAI API message format with a system prompt.

Upload your training file with the OpenAI client, create a fine-tuning job for GPT-3.5 turbo using the file ID, and monitor status, tokens, and costs.

Learn to use the ChatGPT API with Python to automate tasks and build ChatGPT-based technologies, including applications like chatting with a PDF, guided by building blocks for creative use.

Adopt installation lectures as scalable guidelines, focusing on Python prerequisites and understanding over syntax. Use pip to install libraries, noting OpenAI Gym in reinforcement learning contexts.