rohitg00/ai-engineering-from-scratch

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.

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