Beginning Anomaly Detection Using Python-Based Deep Learning
Language: English
Published by APress, US, 2024
- Softcover
- New
Seller: Rarewaves.com USA, London, London, United KingdomRarewaves.com USA
AbeBooks seller since June 11, 2025
Condition: New
£ 51.06
Quantity: Over 20 available
Add to basketItem description from seller
This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning. Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection. After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors. What You Will LearnUnderstand what anomaly detection is, why it it is important, and how it is appliedGrasp the core concepts of machine learning.Master traditional machine learning approaches to anomaly detection using scikit-kearn.Understand deep learning in Python using Keras and PyTorchProcess data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recallApply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications Who This Book Is ForData scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.…
Seller Inventory # LU-9798868800078
- Title
- Beginning Anomaly Detection Using Python-Based Deep Learning
- Author
- Suman Kalyan Adari, Sridhar Alla
- Publisher
- APress, US
- Publication year
- 2024
- Condition
- New
- Binding
- Paperback
- Language
- English
- ISBN 13
- 9798868800078
- Edition
- Second Edition.
This beginner-oriented book will help you understand and perform anomaly detection by learning cutting-edge machine learning and deep learning techniques. This updated second edition focuses on supervised, semi-supervised, and unsupervised approaches to anomaly detection. Over the course of the book, you will learn how to use Keras and PyTorch in practical applications. It also introduces new chapters on GANs and transformers to reflect the latest trends in deep learning.
Beginning Anomaly Detection Using Python-Based Deep Learning begins with an introduction to anomaly detection, its importance, and its applications. It then covers core data science and machine learning modeling concepts before delving into traditional machine learning algorithms such as OC-SVM and Isolation Forest for anomaly detection using scikit-learn. Following this, the authors explain the essentials of machine learning and deep learning, and how to implement multilayer perceptrons for supervised anomaly detection in both Keras and PyTorch. From here, the focus shifts to the applications of deep learning models for anomaly detection, including various types of autoencoders, recurrent neural networks (via LSTM), temporal convolutional networks, and transformers, with the latter three architectures applied to time-series anomaly detection. This edition has a new chapter on GANs (Generative Adversarial Networks), as well as new material covering transformer architecture in the context of time-series anomaly detection.
After completing this book, you will have a thorough understanding of anomaly detection as well as an assortment of methods to approach it in various contexts, including time-series data. Additionally, you will have gained an introduction to scikit-learn, GANs, transformers, Keras, and PyTorch, empowering you to create your own machine learning- or deep learning-based anomaly detectors.
What You Will Learn
- Understand what anomaly detection is, why it it is important, and how it is applied
- Grasp the core concepts of machine learning.
- Master traditional machine learning approaches to anomaly detection using scikit-kearn.
- Understand deep learning in Python using Keras and PyTorch
- Process data through pandas and evaluate your model's performance using metrics like F1-score, precision, and recall
- Apply deep learning to supervised, semi-supervised, and unsupervised anomaly detection tasks for tabular datasets and time series applications
Who This Book Is For Data scientists and machine learning engineers of all levels of experience interested in learning the basics of deep learning applications in anomaly detection.
"Synopsis" may belong to another edition of this title.
About the Author
Suman Kalyan Adari is a machine learning research engineer. He obtained a B.S. in Computer Science at the University of Florida and a M.S. in Computer Science specializing in Machine Learning at Columbia University. He has been conducting deep learning research in adversarial machine learning since his freshman year at the University of Florida and presented at the IEEE Dependable Systems and Networks workshop on Dependable and Secure Machine Learning held in Portland, Oregon in June 2019. Currently, he works on various anomaly detection tasks spanning behavioral tracking and geospatial trajectory modeling.
He is passionate about deep learning, and specializes in various fields ranging from video processing, generative modeling, object tracking, time-series modeling, and more.
Sridhar Alla is the co-founder and CTO of Bluewhale, which helps organizations big and small in building AI-driven big data solutions and analytics, as well as SAS2PY, a powerful tool to automate migration of SAS workloads to Python-based environments using Pandas or PySpark. He is a published author and an avid presenter at numerous conferences, including Strata, Hadoop World, and Spark Summit. He also has several patents filed with the US PTO on large-scale computing and distributed systems. He has extensive hands-on experience in several technologies, including Spark, Flink, Hadoop, AWS, Azure, Tensorflow, Cassandra, and others. He spoke on Anomaly Detection Using Deep Learning at Strata SFO in March 2019 and also presented at Strata London in October 2019. He was born in Hyderabad, India and now lives in New Jersey, USA with his wife Rosie, his daughters Evelyn andMadelyn, and his son, Jayson. When he is not busy writing code, he loves to spend time with his family. He also enjoys training, coaching, and organizing meetups.
"About the title" may belong to another edition of this title.
Rarewaves.com USA
London, London, United Kingdom
AbeBooks seller since June 11, 2025
Shipping rates from United Kingdom to U.S.A.
| Item | 10 to 15 business days | 10 to 15 business days |
|---|---|---|
| First item | £ 0.00 | £ 0.00 |
Payment methods
Seller's business information
RAREWAVES.COM LIMITED
Elsley Court, 20-22 Great Titchfield Street
London, United Kingdom W1W 8BE
Right of withdrawal
If you are a consumer you can withdraw from the contract in accordance with the following. Consumer means any natural person who is acting for purposes which are outside his trade, business, craft or profession.
Information regarding the right of withdrawal
Statutory right to withdraw
You have the right to withdraw from this contract within 14 days without giving any reason.
The withdrawal period will expire after 14 days from the day on which you acquire, or a third party other than the carrier and indicated by you acquires, physical possession of the last good or the last lot or piece.
To exercise the right of withdrawal, electronically fill in and submit a clear statement on our website, under "My Purchases" in "My Account". We will communicate to you an acknowledgement of receipt of such a withdrawal on a durable medium (e.g. by e-mail) without delay.
To meet the withdrawal deadline, it is sufficient for you to send your communication concerning your exercise of the right of withdrawal before the withdrawal period has expired.
Effects of withdrawal
If you withdraw from this contract, we will reimburse to you all payments received from you, including the costs of delivery (except for the supplementary costs arising if you chose a type of delivery other than the least expensive type of standard delivery offered by us).
We may make a deduction from the reimbursement for loss in value of any goods supplied, if the loss is the result of unnecessary handling by you.
We will make the reimbursement without undue delay, and not later than 14 days after the day on which we are informed about your decision to withdraw from this contract.
We will make the reimbursement using the same means of payment as you used for the initial transaction, unless you have expressly agreed otherwise; in any event, you will not incur any fees as a result of such reimbursement.
We may withhold reimbursement until we have received the goods back, or you have supplied evidence of having sent back the goods, whichever is the earliest.
You shall send back the goods or hand them over to Rarewaves.com USA, Unit 144 The Lightbox, 111 Power Road, W4 5PY, London, London, United Kingdom, without undue delay and in any event not later than 14 days from the day on which you communicate your withdrawal from this contract to us. The deadline is met if you send back the goods before the period of 14 days has expired. You will have to bear the direct cost of returning the goods. You are only liable for any diminished value of the goods resulting from the handling other than what is necessary to establish the nature, characteristics and functioning of the goods.
Exceptions to the right of withdrawal
The right of withdrawal does not apply to:
- The delivery of newspapers, journals or magazines with the exception of subscription contracts; and
- The supply of digital content which is not supplied on a tangible medium (e.g. on a CD or DVD) if you accepted when you placed your order that we could start to deliver it, and that you could not withdraw once delivery had started.
Shipping terms
Please note that we do not offer Priority shipping to any country.
We currently do not ship to the below countries:
Russia
Belarus
Ukraine
Please do not attempt to place orders with any of these countries as a ship to address - they will be cancelled.