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Discover how TPOT can be used to handle automation in machine learning and explore the different types of tasks that TPOT can automate Key FeaturesUnderstand parallelism and how to achieve it in Python.Learn how to use neurons, layers, and activation functions and structure an artificial neural network.Tune TPOT models to ensure optimum performance on previously unseen data.Book Description The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level. What you will learnGet to grips with building automated machine learning modelsBuild classification and regression models with impressive accuracy in a short timeDevelop neural network classifiers with AutoML techniquesCompare AutoML models with traditional, manually developed models on the same datasetsCreate robust, production-ready modelsEvaluate automated classification models based on metrics such as accuracy, recall, precision, and f1-scoreGet hands-on with deployment using Flask-RESTful on localhostWho this book is for Data scientists, data analysts, and software developers who are new to machine learning and want to use it in their applications will find this book useful. This book is also for business users looking to automate business tasks with machine learning. Working knowledge of the Python programming language and beginner-level understanding of machine learning are necessary to get started.
Developed by highly respected creators of the evidence-based Pyramid Model for Promoting Social Emotional Competence in Infants and Young Children, the TPOT(TM) tool measures how well teachers are implementing the model in classrooms enrolling children
Available in packs of 5, these are the 24-page forms needed to conduct the Teaching Pyramid Observation Tool (TPOT(tm)) for Preschool Classrooms observational assessment. If your program is among the thousands using the evidence-based Pyramid Model for Promoting Social Emotional Competence in Infants and Young Children, this is the one tool you need to support teachers to effectively use the practices. Developed by highly respected creators of the Pyramid Model for classrooms enrolling children 2-5 years of age, the TPOT is an in-depth tool that provides information on how well teachers are implementing practices related to universal, targeted, and individualized supports. This is the one tool you need to make sure teachers are effectively putting the evidence-based Pyramid Model for Promoting Social Emotional Competence in Infants and Young Children into practice. Learn more about TPOT See which domain of school readiness in the Head Start Child Development and Early Learning Framework this tool addresses. View our recorded webinar: Using the Teaching Pyramid Observation Tool (TPOT(tm)) for Preschool Classrooms presented by Lise Fox, Ph.D., Mary Louise Hemmeter, Ph.D., Patricia Snyder, Ph.D.
This book features high-quality research papers presented at the First Doctoral Symposium on Human Centered Computing (HUMAN 2023), jointly organized by Computer Society of India, Kolkata Chapter and Techno India University, West Bengal, on February 25, 2023. This book discusses the topics of modern human centered computing and its applications. The book showcases the fusion of human sciences (social and cognitive) with computer science (human–computer interaction, signal processing, machine learning, and ubiquitous computing).
This book is a thorough and comprehensive guide to the use of modern data science within health care. Critical to this is the use of big data and its analytical potential to obtain clinical insight into issues that would otherwise have been missed and is central to the application of artificial intelligence. It therefore has numerous uses from diagnosis to treatment. Clinical Applications of Artificial Intelligence in Real-World Data is a critical resource for anyone interested in the use and application of data science within medicine, whether that be researchers in medical data science or clinicians looking for insight into the use of these techniques.
First Prize winner, Oncology Book Category, British Medical Association 2012 Medical Book Competition Deepen your knowledge with a comprehensive, clinical approach to the scientific foundations of radiation oncology and general oncology as well as state-of-the-art techniques and modalities. Implement a multidisciplinary, "team care" approach to providing intricate treatment plans for patients, often in conjunction with medical oncologists, and surgeons. Broaden your understanding of the basic biology of the disease processes. Examine the therapeutic management of specific disease sites based on single-modality and combined-modality approaches. Quickly and easily find critical information thanks to an easily accessible, full-color design with over 800 color figures that clearly depict treatment techniques. Get broad multimodality perspectives and unique insights from a diverse team of respected editors and contributors -many of whom are new to this edition - affiliated with institutions across North America and internationally Access the fully searchable text anywhere, anytime at www.expertconsult.com, along with references, additional images and tables, video clips and more! Stay current with comprehensive updates throughout that include a new chapter on survivorship issues, and additional video clips on treatments such as prostate and penile cancer brachytherapy. Improve outcomes by providing the most effective treatment for each patient with expanded coverage of new modalities and treatment regimens. Understand and comply with the latest staging guidelines. Drs. Gunderson and Tepper give you quick access to all the clinical tools you need to master the newest techniques and modalities in radiation oncology.
These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression Hybrid Structural and Behavioral Diversity Methods in GP Multi-Population Competitive Coevolution for Anticipation of Tax Evasion Evolving Artificial General Intelligence for Video Game Controllers A Detailed Analysis of a PushGP Run Linear Genomes for Structured Programs Neutrality, Robustness, and Evolvability in GP Local Search in GP PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification Relational Structure in Program Synthesis Problems with Analogical Reasoning An Evolutionary Algorithm for Big Data Multi-Class Classification Problems A Generic Framework for Building Dispersion Operators in the Semantic Space Assisting Asset Model Development with Evolutionary Augmentation Building Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.
The two volumes LNCS 9597 and 9598 constitute the refereed conference proceedings of the 19th European Conference on the Applications of Evolutionary Computation, EvoApplications 2016, held in Porto, Portugal, in March/April 2016, co-located with the Evo* 2016 events EuroGP, EvoCOP, and EvoMUSART. The 57 revised full papers presented together with 17 poster papers were carefully reviewed and selected from 115 submissions. EvoApplications 2016 consisted of the following 13 tracks: EvoBAFIN (natural computing methods in business analytics and finance), EvoBIO (evolutionary computation, machine learning and data mining in computational biology), EvoCOMNET (nature-inspired techniques for telecommunication networks and other parallel and distributed systems), EvoCOMPLEX (evolutionary algorithms and complex systems), EvoENERGY (evolutionary computation in energy applications), EvoGAMES (bio-inspired algorithms in games), EvoIASP (evolutionary computation in image analysis, signal processing, and pattern recognition), EvoINDUSTRY (nature-inspired techniques in industrial settings), EvoNUM (bio-inspired algorithms for continuous parameter optimization), EvoPAR (parallel implementation of evolutionary algorithms), EvoRISK (computational intelligence for risk management, security and defence applications), EvoROBOT (evolutionary robotics), and EvoSTOC (evolutionary algorithms in stochastic and dynamic environments).