Kniha AI Engineering Husn Ara

AI Engineering

Building Multi-Modal Intelligent Systems with Vision, Language, and Audio From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deployment

Autor: Husn Ara
Jazyk: Angličtina
Väzba: Brožovaná
Dostupnosť: Skladom u dodávateľa
Odosielame za 9-15 dní
30.86
AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and AudioFrom LLM Fi...

Informácie o knihe

Autor
Jazyk
Angličtina
Väzba
Kniha - Brožovaná
Vydalo
2025
Stránok
296
EAN
9798296089038
Enbook ID
50705556
Hmotnosť
400
Rozmery
152 x 229 x 16

Kompletný popis

AI Engineering: Building Multi-Modal Intelligent Systems with Vision, Language, and Audio
From LLM Fine-Tuning to Voice Agents, AR Interfaces, and Real-World Deployment

Unlock the future of artificial intelligence with practical, production-ready multi-modal engineering.

This hands-on guide is built for developers, researchers, and AI professionals who want to go beyond chatbots and dive into building intelligent systems that understand text, images, audio, and human intent - all in one pipeline.

Whether you're fine-tuning large language models (LLMs) or creating voice-driven AR interfaces, this book walks you through the real engineering decisions, tools, and architectures needed to bring multi-modal AI to life.


What You'll Learn:
  • Fine-tuning Large Language Models (LLMs): Train and adapt models like GPT-2, LLaMA, and Mistral for custom tasks using Hugging Face, LoRA, QLoRA, and PEFT.

  • Voice Interfaces: Combine Whisper, LLMs, and Bark/Tortoise TTS to build interactive speech-driven assistants.

  • Computer Vision + Language: Use models like BLIP, CLIP, and DETR to connect what systems see to what they say and understand.

  • Instruction Tuning & Hyperparameter Optimization: Build smarter, domain-specific models with efficient training workflows.

  • Multi-Modal Pipelines: Chain audio, image, and text inputs for question answering, summarization, tutoring, and AR/robotic control.

  • Real-Time Interfaces: Deploy intelligent agents using FastAPI, Streamlit, Gradio, Docker, and Hugging Face Spaces.

  • Edge & Offline Deployment: Optimize models with ONNX, quantization (4-bit, 8-bit), and TensorRT for low-latency inference on CPU/GPU.


Use Cases Covered:
  • Smart document summarizers with OCR + TTS

  • Voice-enabled image assistants

  • Emotion-aware agents

  • Virtual tutors

  • AR-enhanced AI interfaces

  • Robotic perception + control from voice/image input

  • Secure, multilingual, and privacy-conscious AI systems


Tools & Frameworks Inside:
  • Python, PyTorch, Hugging Face Transformers

  • LangChain, OpenCV, Whisper, TTS, BLIP

  • ROS, Unity (AR/VR), Gradio, Streamlit

  • Docker, FastAPI, gRPC, TorchServe

Built for engineers. Written with depth. Designed for real-world impact.

If you're ready to build intelligent multi-modal agents that understand the world like humans do - across speech, vision, and language - this book gives you the complete roadmap.

Perfect for:
Machine learning engineers, data scientists, AI product developers, researchers, robotics engineers, and anyone building cutting-edge AI systems.

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