Training VAEs involves feeding batches of images through the encoder-decoder pipeline, minimizing the combined loss. The model learns unsupervised – no labels needed beyond the images themselves. Start with simple architectures (CNNs for encoders/decoders) and scale up. Watch for posterior collapse, where the KL term dominates, causing σ→0 and the latent space to become useless…

Loss and Reparameterization VAEs optimize a cleverly designed loss function that balances two goals. First, reconstruction loss measures how well the decoder rebuilds the original input, often using mean squared error for images:Lrecon=∥x−x^∥2 where x is the input and x^ the reconstruction. Second, KL divergence regularizes the latent distribution to stay close to a standard…

While language models have dominated AI discussions lately, the image generation revolution happening in parallel deserves equal attention. At the heart of many breakthrough image synthesis systems, from Stable Diffusion to DALL-E, lies a powerful architecture called the Variational Autoencoder (VAE). VAEs serve as the compression engine that makes modern image generation practical, transforming high-dimensional…

You might not have heard of the term foundation model before, but you’ve almost certainly used one. In reference to LLMs (and AI in general), “foundation model” refers to a model that has been trained on vast swathes of data, such that it can be used in a general context. LLM foundation models are those…

RAG, or retrieval-augmented generation, is a technique that allows LLMs to access external sources of data. Normally, LLMs can only rely on the prompt fed to them, and the knowledge baked into their parameters. However, RAG vastly expands LLMs’ capabilities. Using these external sources of data, an LLM can incorporate it into its output. This…

What is Transfer Learning? Transfer learning is a machine learning technique where a model trained on one task is reused as a starting point for a different but related task. Instead of building and training a new model from scratch for every problem, transfer learning leverages the knowledge and features learned by a pre-trained model…

LoRA in a Nutshell Low-Rank Adaptation, or LoRA, is a cutting-edge technique designed to fine-tune large language models (LLMs) efficiently and effectively. Instead of modifying the entire massive model, LoRA adapts just a small fraction of parameters through lightweight additions, enabling rapid specialization without retraining from scratch or requiring excessive computational resources. The Core Idea:…

With all the buzz around OpenAI’s new Sora 2 video generation model, you might be wondering what makes it different from other previous SOTA models like Veo 3. Here’s the breakdown. Visual fidelity: Sora 2 has made improvements in visual fidelity, generating frames natively in 720p and then upscaling, to maintain sharp textures and object…

Core Building Blocks Why Agentic AI?