Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch - Image 1

£38.53

Apress Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch

Name: Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch
Brand: Apress
Price: 38.98 GBP
Availability: InStock

AI & Machine Learning

Price History & Forecast

Last 30 days • 30 data points (No recent data available)

Historical

Generating forecast...

Price Distribution

Price distribution over 30 days • 2 price levels

Days at Price

Current Price

Price Analysis

Most common price: £39 (29 days, 96.7%)

Price range: £39 - £39

Price levels: 2 different prices over 30 days

Description

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.

Product Specifications

Brand: Apress
Format: paperback
ASIN: B0CJ2BH9HD
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 02 January 2024
Listed Since: 14 September 2023

Barcode

No barcode data available

Apress Beginning Anomaly Detection Using Python-Based Deep Learning: Implement Anomaly Detection Applications with Keras and PyTorch

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow

Finding Ghosts in Your Data: Anomaly Detection Techniques with Examples in Python

Handbook of Anomaly Detection: Cutting-edge Methods and Hands-On Code Examples

Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Understanding Deep Learning: Building Machine Learning Systems with PyTorch and TensorFlow: From Neural Networks (CNN, DNN, GNN, RNN, ANN, LSTM, GAN) to Natural Language Processing (NLP)

Applied Deep Learning with TensorFlow 2: Learn to Implement Advanced Deep Learning Techniques with Python

Time Series Analysis with Python Cookbook: Practical recipes for exploratory data analysis, data preparation, forecasting, and model evaluation

Time Series Analysis with Python Cookbook: Practical recipes for the complete time series workflow, from modern data engineering to advanced forecasting and anomaly detection

Deep Learning with Python: A Hands-on Introduction

Hands-on Machine Learning with Python: Implement Neural Network Solutions with Scikit-learn and PyTorch

Practical Deep Learning, 2nd Edition: A Python-Based Introduction

Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas

Modern Time Series Forecasting Techniques For Predictive Analytics and Anomaly Detection: From Classical Foundations to Cutting-Edge Applications

Modern Computer Vision with PyTorch: Explore deep learning concepts and implement over 50 real-world image applications

Deep Learning and AI Superhero: Mastering TensorFlow, Keras, and PyTorch: Advanced Machine Learning and AI, Neural Networks, and Real-World Projects (Mastering the AI Revolution)

Mastering PyTorch: Build powerful neural network architectures using advanced PyTorch 1.x features

Hands–On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data

Practical Deep Learning for Computer Vision with Python

A Greater Foundation for Machine Learning Engineering: The Hallmarks of the Great Beyond in Pytorch, R, Tensorflow, and Python

Artificial Intelligence for Predictive Maintenance (Mechanical Engineering Essentials with Python)

Practical Deep Learning with Python: A Hands-On Introduction: A Python-Based Introduction

CRC Press Applied Cloud Deep Semantic Recognition Book

Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples