Search Results for "function point"

Generative AI is a comprehensive collection of code samples, notebooks, and demo applications designed to help developers build generative-AI workflows on the Vertex AI platform. It spans multiple modalities—text, image, audio, search (RAG/grounding) and more—showing how to integrate foundation models like the Gemini family into cloud projects. The README emphasises getting started with prompts, datasets, environments and sample apps, making it ideal for both experimentation and...

Code-Mode is a plug-and-play library that lets AI agents call tools by executing TypeScript (or via a Python wrapper) instead of making many individual function calls. Its core philosophy is that language models are very good at writing code, so rather than exposing hundreds of separate tool endpoints, you give the model a single “code execution” tool that has access to your full toolkit through code. This approach can dramatically reduce the number of tool-call iterations needed in complex...

Generative AI for Beginners is a 21-lesson course by Microsoft Cloud Advocates that teaches the fundamentals of building generative AI applications in a practical, project-oriented way. Lessons are split into “Learn” modules for core concepts and “Build” modules with hands-on code in Python and TypeScript, so you can jump in at any point that matches your goals. The course covers everything from model selection, prompt engineering, and chat/text/image app patterns to secure development practices and UX for AI. It also walks through modern application techniques such as function calling, RAG with vector databases, working with open source models, agents, fine-tuning, and using SLMs. ...

...While it does not match the fine detail of some slower methods, the tradeoff in speed makes it practical for prototyping and interactive 3D generation. The repository includes inference scripts, utilities for converting point clouds to meshes (e.g. via signed distance function regression), sample notebooks, and weight checkpoints. It also provides documentation on limitations, usage instructions, and example outputs.

High level network definitions with pre-trained weights in TensorFlow (tested with 2.1.0 >= TF >= 1.4.0). Applicability. Many people already have their own ML workflows and want to put a new model on their workflows. TensorNets can be easily plugged together because it is designed as simple functional interfaces without custom classes. Manageability. Models are written in tf.contrib.layers, which is lightweight like PyTorch and Keras, and allows for ease of accessibility to every weight and...