Build AI applications using cutting-edge frameworks like PyTorch, TensorFlow, and Hugging Face
Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Large Language Models (LLMs) like GPT, BERT, and Llama 2 for real-world AI projects
Advanced AI topics, including MLOps, multimodal AI, and Edge AI deployment
Hands-on projects in chatbots, autonomous agents, generative AI, and time-series forecasting
Book DescriptionArtificial Intelligence with Python, Third Edition is a complete, hands-on guide that takes you from the foundations of AI to mastering cutting-edge techniques, including Large Language Models (LLMs), Reinforcement Learning, and MLOps. This fully updated edition ensures you stay ahead with the latest AI innovations.
You'll explore machine learning algorithms, neural networks, and state-of-the-art deep learning architectures like transformers and diffusion models. You'll also learn to fine-tune open-source LLMs, work with multimodal AI, and deploy models efficiently using MLOps best practices.
Unlike many AI books that focus heavily on theoretical foundations and academic algorithms, this book is designed as a practitioner's guide to mastering AI for real-world applications. Rather than overwhelming you with every algorithm ever created, this book prioritizes the AI techniques that matter most in industry today-from Large Language Models (LLMs) and Reinforcement Learning to MLOps and Edge AI. Whether you're a data scientist, ML engineer, or software developer, this book serves as your hands-on roadmap to building AI-powered applications, fine-tuning models, and deploying AI at scale.
By the end of this book, you'll have hands-on experience in building AI-powered applications, deploying models on the cloud and edge devices.What you will learn
Implement deep learning with PyTorch and TensorFlow, including CNNs and transformers
Work with Large Language Models (LLMs) like GPT, BERT, and T5 for NLP tasks
Build AI-powered chatbots, recommendation systems, and intelligent agents
Apply AI techniques to real-world domains, including speech recognition and time-series forecasting
Deploy AI models using FastAPI, Docker, and MLOps tools like MLflow
Explore Edge AI by running models on Jetson Nano and Raspberry Pi
Who this book is forThis book is for data scientists, AI engineers, machine learning practitioners, and software developers looking to master AI using Python. Basic programming knowledge in Python is recommended, but all AI and machine learning concepts are explained from the ground up, making it accessible to both beginners and experienced professionals.
Auflage
Sprache
Verlagsort
Editions-Typ
Maße
Höhe: 235 mm
Breite: 191 mm
ISBN-13
978-1-80580-461-1 (9781805804611)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Alberto Artasanchez is a solutions architect with expertise in the cloud, data solutions, and machine learning, with a career spanning over 28 years in various industries. He is an AWS Ambassador and publishes frequently in a variety of cloud and data science publications. He is often tapped as a speaker on topics including data science, big data, and analytics. He has a strong and extensive track record of designing and building end-to-end machine learning platforms at scale. He also has a long track record of leading data engineering teams and mentoring, coaching, and motivating them. He has a great understanding of how technology drives business value and has a passion for creating elegant solutions to complicated problems. Head of AI at Cyber, Math PhD, Scientific Content Creator, Lecturer. With over two decades of immersion in data science and machine learning, my journey has been marked by a relentless pursuit of pioneering AI-driven products at Stealth Mode and providing strategic AI counseling.
Table of Contents
Introduction to Artificial Intelligence
Essential Mathematics for AI
Core AI Concepts & Techniques
Supervised Learning with Python
Unsupervised Learning & Clustering
Ensemble Learning & Model Stacking
Neural Networks & Deep Learning Fundamentals
Convolutional Neural Networks (CNNs) for Image Recognition
Recurrent Neural Networks (RNNs) and Transformers
Generative AI & Large Language Models (LLMs)
Building a Modern AI Data Pipeline
MLOps & AI Model Deployment
Building Intelligent Agents with Reinforcement Learning
Multimodal AI & Generative Media
Edge AI & On-Device Machine Learning
AI and Big Data
AI Ethics, Bias, and Responsible AI
Building AI-Powered Chatbots
Speech Recognition & AI Voice Assistants
AI for Time Series & Forecasting
Autonomous AI Agents & Workflow Automation
Future Trends & Next-Gen AI
AI Career Pathways: Roles, Skills, and Certifications
Preparing for AI Interviews
