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Beginning Anomaly Detection Using Python-Based Deep Learning
Utilize this easy-to-follow beginner's guide to understand how deep learning can be applied to the task of anomaly detection. Using Keras and PyTorch in Python, the book focuses on how various deep learning models can be applied to semi-supervised and unsupervised anomaly detection tasks. This book begins with an explanation of what anomaly detection is, what it is used for, and its importance. After covering statistical and traditional machine learning methods for anomaly detection using Scikit-Learn in Python, the book then provides an introduction to deep learning with details on how to build and train a deep learning model in both Keras and PyTorch before shifting the focus to applicatio...
Beginning MLOps with MLFlow
Integrate MLOps principles into existing or future projects using MLFlow, operationalize your models, and deploy them in AWS SageMaker, Google Cloud, and Microsoft Azure. This book guides you through the process of data analysis, model construction, and training. The authors begin by introducing you to basic data analysis on a credit card data set and teach you how to analyze the features and their relationships to the target variable. You will learn how to build logistic regression models in scikit-learn and PySpark, and you will go through the process of hyperparameter tuning with a validation data set. You will explore three different deployment setups of machine learning models with v...
Practical Computer Vision Applications Using Deep Learning with CNNs
Deploy deep learning applications into production across multiple platforms. You will work on computer vision applications that use the convolutional neural network (CNN) deep learning model and Python. This book starts by explaining the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way you will cover artificial neural networks (ANNs), building one from scratch in Python, before optimizing it using genetic algorithms. For automating the process, the book highlights the limitations of traditional hand-crafted features for computer vision and why the CNN deep-learning model is the state-of-art solution. CNNs are discussed from scratch to demonstrate ...
Practical Machine Learning: A New Look at Anomaly Detection
Finding Data Anomalies You Didn't Know to Look For Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what “suspects” you’re looking for. This O’Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The conc...
Deploy Machine Learning Models to Production
Build and deploy machine learning and deep learning models in production with end-to-end examples. This book begins with a focus on the machine learning model deployment process and its related challenges. Next, it covers the process of building and deploying machine learning models using different web frameworks such as Flask and Streamlit. A chapter on Docker follows and covers how to package and containerize machine learning models. The book also illustrates how to build and train machine learning and deep learning models at scale using Kubernetes. The book is a good starting point for people who want to move to the next level of machine learning by taking pre-built models and deploying t...
Industrial Robotics
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Introducing MLOps
More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cyc...
Automatisierte Einzelentscheidungen im (Schweizer) Datenschutzrecht
Die vorliegende Arbeit widmet sich dem Phänomen, der Entstehungsgeschichte, den Regelungszielen und den Tatbestandsmerkmalen der Regulierung automatisierter Einzelentscheidungen im Datenschutzrecht. Der erste Teil vermittelt einen umfassenden Einblick in das Phänomen der automatisierten Entscheidungen und beleuchtet deren gesellschaftliche und rechtliche Debatte sowie die Rolle von Algorithmen und die technischen Grundlagen des maschinellen Lernens. Der zweite Teil, der auf das Datenschutzrecht fokussiert, zeichnet die geschichtliche Entwicklung der Regelung automatisierter Einzelentscheidungen nach, von den Anfängen in Frankreich und der Übernahme durch die Europäische Union und den Europarat bis hin zur Integration im Schweizer Recht. Anschliessend werden die mit der Regelung automatisierter Einzelentscheidungen verfolgten Ziele und die Gründe für die Normierung im Datenschutzrecht untersucht. Auf dieser Grundlage wird der Begriff der «automatisierten Einzelentscheidung» definiert, die Tatbestandsmerkmale analysiert und ein Prüfschema zur Identifizierung solcher Entscheidungen entwickelt.
Big Data Analytics with Hadoop 3
Explore big data concepts, platforms, analytics, and their applications using the power of Hadoop 3 Key Features Learn Hadoop 3 to build effective big data analytics solutions on-premise and on cloud Integrate Hadoop with other big data tools such as R, Python, Apache Spark, and Apache Flink Exploit big data using Hadoop 3 with real-world examples Book Description Apache Hadoop is the most popular platform for big data processing, and can be combined with a host of other big data tools to build powerful analytics solutions. Big Data Analytics with Hadoop 3 shows you how to do just that, by providing insights into the software as well as its benefits with the help of practical examples. Once ...
Practical Machine Learning for Computer Vision
This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust...
