Daily Dose of Data Science | Avi Chawla | Substack·1yA crash course on RAG systems—Part 5
Part 5 of the RAG crash course focuses on the implementation of key components for multimodal RAG systems, such as CLIP embeddings, multimodal prompting, and tool calling. The series aims to educate readers on building reliable RAG systems that can reduce costs and handle complex data types, ultimately aiding businesses in achieving greater impact.
Towards AI·1yLLaMA explained !
LLaMA, an advanced open-source large language model by Meta, brings several enhancements over conventional transformer architecture, such as grouped multi-query attention, RMS normalization, and rotary positional embeddings. These innovations result in more efficient computation and dynamic learning capabilities, making LLaMA a competitive choice for large language model applications.
Medium·1yHow Neural Networks Learn: A Probabilistic Viewpoint
Understanding concepts like entropy, cross-entropy, and KL-Divergence is crucial for training neural networks. These measures help in quantifying similarities or divergences between probability distributions. By interpreting models probabilistically, practitioners can define objective functions — commonly known as loss functions — that need to be minimized during model training, often using gradient descent methods facilitated by frameworks like PyTorch.