RNN vs. CNN vs. Autoencoder vs. Attention/Transformer: A Practical Guide with PyTorch Deep learning has evolved rapidly, offering a toolkit of neural architectures for various data types and tasks. Among the most influential are Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Autoencoders, and the modern Attention/Transformer models.But how do Read more
Detailed Notes: RAG, Agents, and Memory in AI Applications 1. Task Solving and Context Instructions vs. Context: Instructions: General, static for the application (how to solve). Context: Specific to each query (what info to use). Missing context → AI more likely to hallucinate or err. Two Major Context Patterns: RAG Read more
Prompt Engineering: Detailed Notes 1. What is Prompt Engineering? Prompt engineering is the craft of designing instructions (prompts) to guide AI models to produce desired outputs. It is the simplest and most common adaptation technique—no model weights are changed (unlike finetuning). Effective prompt engineering is systematic and should be treated Read more
Chapter 2: Understanding Foundation Models Overview Foundation model design choices (training data, architecture/size, post-training) are increasingly opaque. The training process splits into pre-training (makes model capable) and post-training (aligns model to human preferences). Sampling (how outputs are chosen from all possibilities) is a crucial, often-underestimated factor impacting model behavior and Read more
Chapter 1 Summary: Introduction to Building AI Applications with Foundation Models 1. The Scaling Up of AI Models Foundation models like ChatGPT and Gemini are highly powerful but require enormous compute resources and vast amounts of data. Only a few organizations have the capability to train these models, leading to Read more