rohitg00/ai-engineering-from-scratch
Ai Engineering From Scratch
This project is a structured AI engineering curriculum and educational program designed to teach the construction of machine learning models, neural networks, and autonomous agents from the ground up. It serves as a comprehensive machine learning course covering mathematical foundations, deep learning architectures, and reinforcement learning through practical implementation.
The project provides a technical framework for building autonomous loops and memory systems via an agent framework, as well as guides for implementing multimodal AI systems that integrate vision, audio, and text processing. It includes a blueprint for AI infrastructure deployment, focusing on quantization, inference optimization, and GPU autoscaling for production environments.
The curriculum is supported by technical tools for knowledge assessment, including quizzes that generate personalized learning paths. It covers a broad range of capabilities including natural language processing, computer vision, AI safety and alignment, and the integration of large language models through standardized API clients.
Features
- AI Agent - Provides a framework for building autonomous loops and memory systems that coordinate multiple agents via tool use.
- Machine Learning Courses - Offers a structured training program covering mathematical foundations, deep learning architectures, and reinforcement learning through practical implementation.
- Machine Learning Fundamentals - Teaches the fundamental mathematical and technical foundations of machine learning and deep learning through practical builds.
- Agent Skill Extensions - Ships a CLI tool to deploy a library of specialized prompts and skills into compatible agent directories.
- Agentic LLM Frameworks - Implements a framework for designing autonomous loops, memory systems, and tool integration for large language models.
- Agent Configuration Schemas - Implements standardized configuration schemas to create reproducible environments for autonomous AI agents.
- AI Agent Skills - Provides a mechanism to deploy reusable prompts and skill definitions into AI assistants for expert-level task execution.
- Language Model Integrations - Implements standardized API clients to connect to external AI providers and manage prompt interactions.
- Machine Learning Foundations - Focuses on the mathematical and theoretical foundations required to implement machine learning models from scratch.
- Machine Learning Implementations - Guides the implementation of classical algorithms and neural network architectures from scratch.
- Modular Pipeline Orchestrators - Structures neural networks and multimodal AI workflows as a sequence of discrete, modular processing stages.
- Neural Network Implementations - Teaches the implementation of neural networks and ML pipelines using tensors and transformers from the ground up.
- Prompt Templates - Provides a system for distributing and managing reusable prompt templates and tool definitions for AI assistants.
- Autonomous AI Agents - Teaches the construction of autonomous loops and memory systems for coordinating multiple AI agents.
- Environment Scaffolding - Initializes a standardized set of schemas and scripts to manage autonomous AI agent development.
- Agent Workbench Scaffolding - Sets up a reusable management framework including schemas and handoff scripts for autonomous AI agents.
- AI Inference Infrastructure - Provides blueprints for configuring inference optimization, GPU autoscaling, and quantization for production AI deployments.
- Knowledge Assessments - Implements interactive quizzes and knowledge assessments to determine a learner's starting point in the curriculum.
- Learning Paths - Generates customized, sequenced study plans based on individual assessment scores and time estimates.
- AI & Machine Learning Education - Offers a structured educational sequence covering everything from basic mathematics to advanced agent engineering.
- Technical Learning Paths - Designs structured learning paths and automated assessments using a build-and-learn pedagogical approach.
- Technical Proficiency Assessments - Evaluates technical proficiency through targeted assessments to map personalized learning trajectories.
- Audio-Language Model Implementations - Implements waveform analysis and integrates speech-to-text and text-to-speech models for sound data processing.
- Computer Vision - Provides a guide for implementing image classification and object detection pipelines using deep learning toolkits.
- Multimodal AI Tutorials - Provides technical instructions and educational content for implementing unified vision, audio, and text processing pipelines.
- Multimodal Model Integrations - Teaches how to integrate vision, audio, and text into unified multimodal AI models.
- Multimodal Processing - Implements libraries to handle diverse input formats including text, images, and audio within shared embedding spaces.
- Natural Language Processing - Covers the development of text processing systems, embeddings, transformers, and large language models.
- Reinforcement Learning - Provides practical exercises for implementing reinforcement learning concepts including Q-Learning, PPO, and RLHF.
- Multimodal AI - Guides the development of AI systems that bridge text with images and audio modalities.
- LLM Clients - Implements standardized API clients and skill definitions for managing interactions with LLM providers.
- AI Infrastructure - Provides a blueprint for production deployment including GPU autoscaling, quantization, and inference optimization.
- AI-Driven Personalization - Provides an AI-driven engine that calculates personalized learning trajectories based on user quiz performance.
