Decoding GPT

Explore how biological neurons inspire neural networks and deep learning, from dendrites and synapses to perceptrons with weights and biases that produce outputs via thresholds, including 'fire together, wire together'.

Trace deep learning from the perceptron and universal approximation theorem to multi-layer networks, with convolutional networks for images and recurrent networks for language, and LSTM/GRU gates with attention.

Explore the transformer architecture, including encoder–decoder blocks, self-attention and multi-head attention, with positional encoding and input embeddings that enable parallel processing and outperformed prevailing models in English-to-German translation.

Delve into the encoder–decoder transformer with input embedding, positional encoding, self-attention and multi-head attention, and learn training, masking, and fine-tuning concepts.

Explore decoding GPT with ChatGPT hands-on techniques, including prompt engineering, role prompts, and features like grammar correction, SQL conversion, and code generation.

Master programmatic access to the OpenAI API, compare it with ChatGPT for data privacy and pricing, and learn setup steps, models, tokens, and temperature to generate responses.

Train a custom model by fine-tuning a general bot with your data using transfer learning, vector indices, and embeddings, then deploy via OpenAI API and LangChain for domain Q&A.

Explore the future of generative AI and the evolving GPT landscape from GPT-3 to GPT-4. Trace AI hype cycles, transformers, and milestone moments to assess the tipping point.

Explore how GPT-4 expands multimodal input, coding in all languages with a 25,000-word context, and copilot innovations for code and office tasks, while considering future AI and AGI implications.