Learn Artificial Intelligence : Boost Your Productivity
Sumit Dhariwal
Sold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
New - Hardcover
Condition: New
Ships from Germany to U.S.A.
Quantity: 2 available
Add to basketSold by AHA-BUCH GmbH, Einbeck, Germany
AbeBooks Seller since August 14, 2006
Condition: New
Quantity: 2 available
Add to basketNeuware - Welcome to Learn Artificial Intelligence: Boost Your Productivity is a detailed guide to modern AI technologies. Beginners to specialists can use this book. AI's theory and applications must be understood as it evolves and affects many industries.We begin by reviewing AI and machine learning's history, key concepts, ethics, and developments. Python, the basic AI programming language, encompasses data structures, control flows, functions, object-oriented programming, text files, databases, and significant data science tools. Pretreatment and exploratory analysis with Titanic dataset samples are covered in the book.Data exploration and predictive modelling are covered in supervised and unsupervised learning. Then we discuss convolutional and recurrent neural networks and deep learning applications. Sequence models like RNNs, LSTMs, and transformers are examined.GANs, VAEs, their training, and applications will be covered, followed by reinforcement learning, which focusses agent training through incentives and penalties. NLP includes text prep, translation, summarisation, and sentiment analysis.Practical AI application chapters address model deployment, monitoring, maintenance, and industry case studies. The final chapter covers AI ethics, bias, transparency, privacy, and future advances.AI principles to cutting-edge methods are covered in this book. This book equips students, researchers, and industry professionals to excel in this dynamic field.
Seller Inventory # 9798899063848
Welcome to Learn Artificial Intelligence: Boost Your Productivity is a detailed guide to modern AI technologies. Beginners to specialists can use this book. AI's theory and applications must be understood as it evolves and affects many industries.
We begin by reviewing AI and machine learning's history, key concepts, ethics, and developments. Python, the basic AI programming language, encompasses data structures, control flows, functions, object-oriented programming, text files, databases, and significant data science tools. Pretreatment and exploratory analysis with Titanic dataset samples are covered in the book.
Data exploration and predictive modelling are covered in supervised and unsupervised learning. Then we discuss convolutional and recurrent neural networks and deep learning applications. Sequence models like RNNs, LSTMs, and transformers are examined.
GANs, VAEs, their training, and applications will be covered, followed by reinforcement learning, which focusses agent training through incentives and penalties. NLP includes text prep, translation, summarisation, and sentiment analysis.
Practical AI application chapters address model deployment, monitoring, maintenance, and industry case studies. The final chapter covers AI ethics, bias, transparency, privacy, and future advances.
AI principles to cutting-edge methods are covered in this book. This book equips students, researchers, and industry professionals to excel in this dynamic field.
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