- Start
- Product
[CAN] Artificial Intelligence with Python, Third Edition
Your complete guide to building intelligent apps using Python
Packt Publishing
3rd Edition
Published on 30. December 2024
Book
Paperback/Softback
204 pages
978-1-83588-006-7 (ISBN)
Description
Key Features
Up-to-date with the latest developments in AI
Includes more examples and scripts based on PyTorch.
AI on the Cloud updated with modern techniques on AWS.
Includes AIRMA and Transformer Time Series Model
Book DescriptionArtificial Intelligence with Python, 3E is an updated and expanded version of the bestselling guide to artificial intelligence with python, 2E using the latest version of Python 3.11 and AI libraries & techniques. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the latest tools you need to create your own applications.
This edition also includes 4 new chapters on more advanced concepts of Artificial Intelligence, including Generative AI; Large Language Models (LLM); Transformer Model; and AutoML. The book will also cover applications of LLM with RAG, VectorDB and LangChain to build intelligent AI applications
What you will learn
Who this book is for
Up-to-date with the latest developments in AI
Includes more examples and scripts based on PyTorch.
AI on the Cloud updated with modern techniques on AWS.
Includes AIRMA and Transformer Time Series Model
Book DescriptionArtificial Intelligence with Python, 3E is an updated and expanded version of the bestselling guide to artificial intelligence with python, 2E using the latest version of Python 3.11 and AI libraries & techniques. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the latest tools you need to create your own applications.
This edition also includes 4 new chapters on more advanced concepts of Artificial Intelligence, including Generative AI; Large Language Models (LLM); Transformer Model; and AutoML. The book will also cover applications of LLM with RAG, VectorDB and LangChain to build intelligent AI applications
What you will learn
Who this book is for
More details
Edition
3rd Revised edition
Language
English
Place of publication
Birmingham
United Kingdom
Edition type
Revised edition
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-83588-006-7 (9781835880067)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Persons
Prateek Joshi is the founder of Plutoshift and a published author of 9 books on Artificial Intelligence. He has been featured on Forbes 30 Under 30, NBC, Bloomberg, CNBC, TechCrunch, and The Business Journals. He has been an invited speaker at conferences such as TEDx, Global Big Data Conference, Machine Learning Developers Conference, and Silicon Valley Deep Learning. Apart from Artificial Intelligence, some of the topics that excite him are number theory, cryptography, and quantum computing. His greater goal is to make Artificial Intelligence accessible to everyone so that it can impact billions of people around the world. 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.
Content
Table of Contents
Introduction to Artificial Intelligence
Fundamental Use Cases for Artificial Intelligence
Machine Learning Pipelines
Feature Selection and Feature Engineering
Classification and Regression Using Supervised Learning
Predictive Analytics with Ensemble Learning
Detecting Patterns with Unsupervised Learning
Auto ML
Building Recommender Systems
Heuristic Search Techniques
Artificial Intelligence on the Cloud
Building Games with Artificial Intelligence
Building a Speech Recognizer
Natural Language Processing
Neural Networks
Deep Learning with Convolutional Neural Networks
Recurrent Neural Networks
Transformer Model
Generative AI
Sequential Data and Time Series Analysis
Image Recognition
Large Language Models
Creating Intelligent Agents with Reinforcement Learning
Artificial Intelligence and Big Data
Introduction to Artificial Intelligence
Fundamental Use Cases for Artificial Intelligence
Machine Learning Pipelines
Feature Selection and Feature Engineering
Classification and Regression Using Supervised Learning
Predictive Analytics with Ensemble Learning
Detecting Patterns with Unsupervised Learning
Auto ML
Building Recommender Systems
Heuristic Search Techniques
Artificial Intelligence on the Cloud
Building Games with Artificial Intelligence
Building a Speech Recognizer
Natural Language Processing
Neural Networks
Deep Learning with Convolutional Neural Networks
Recurrent Neural Networks
Transformer Model
Generative AI
Sequential Data and Time Series Analysis
Image Recognition
Large Language Models
Creating Intelligent Agents with Reinforcement Learning
Artificial Intelligence and Big Data