Practical Artificial Intelligence: Essentials of Deep Learning and Neural Network Algorithms
Nonfiction, Computers, Advanced Computing, Engineering, Neural Networks, Computer Engineering, Artificial Intelligence
| Author: | Christian Finlay | ISBN: | 1230002996592 |
| Publisher: | Christian Finlay | Publication: | December 18, 2018 |
| Imprint: | Language: | English |
| Author: | Christian Finlay |
| ISBN: | 1230002996592 |
| Publisher: | Christian Finlay |
| Publication: | December 18, 2018 |
| Imprint: | |
| Language: | English |
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated edition also includes new material on deep learning.
Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, etc.
Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.
This accessible and engaging textbook presents a concise introduction to the exciting field of artificial intelligence (AI). The broad-ranging discussion covers the key subdisciplines within the field, describing practical algorithms and concrete applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks, and reinforcement learning. Fully revised and updated, this much-anticipated edition also includes new material on deep learning.
Topics and features: presents an application-focused and hands-on approach to learning, with supplementary teaching resources provided at an associated website; contains numerous study exercises and solutions, highlighted examples, definitions, theorems; reports on developments in deep learning, including applications of neural networks to generate creative content such as text, etc.
Ideal for foundation courses or modules on AI, this easy-to-read textbook offers an excellent overview of the field for students of computer science and other technical disciplines, requiring no more than a high-school level of knowledge of mathematics to understand the material.
