Session Notebook

NOTEBOOK FOR THERAPISTS & COUNSELLORS: FOR SESSION NOTES, SUPERVISION THEMES AND MORE This notebook is ideal to keep all your client notes in one place, as well as jot down themes to take to supervision, client resources needed for the next session, and your thoughts on theories to research raised by the session. It

Data Science: A First Introduction focuses on using the R programming language in Jupyter notebooks to perform data manipulation and cleaning, create effective visualizations, and extract insights from data using classification, regression, clustering, and inference. The text emphasizes workflows that are clear, reproducible, and shareable, and includes coverage of the basics of version control. All source code is available online, demonstrating the use of good reproducible project workflows. Based on educational research and active learning principles, the book uses a modern approach to R and includes accompanying autograded Jupyter worksheets for interactive, self-directed learning. The book will leave readers well-prepared for data science projects. The book is designed for learners from all disciplines with minimal prior knowledge of mathematics and programming. The authors have honed the material through years of experience teaching thousands of undergraduates in the University of British Columbia’s DSCI100: Introduction to Data Science course.

Unlike any book youve ever read, Mentored by a Millionaire is made up of fifteen mentoring sessions in which you will be mentored in the strategies, skills, and techniques used by super achievers who have become the worlds most successful men and women. You will be mentored in these sessions by Steve Scott, a man who has not only made millions himself, but has helped dozens of others make millions as well.

See a Mesos-based big data stack created and the components used. You will use currently available Apache full and incubating systems. The components are introduced by example and you learn how they work together. In the Complete Guide to Open Source Big Data Stack, the author begins by creating a private cloud and then installs and examines Apache Brooklyn. After that, he uses each chapter to introduce one piece of the big data stack—sharing how to source the software and how to install it. You learn by simple example, step by step and chapter by chapter, as a real big data stack is created. The book concentrates on Apache-based systems and shares detailed examples of cloud storage, release management, resource management, processing, queuing, frameworks, data visualization, and more. What You’ll Learn Install a private cloud onto the local cluster using Apache cloud stack Source, install, and configure Apache: Brooklyn, Mesos, Kafka, and Zeppelin See how Brooklyn can be used to install Mule ESB on a cluster and Cassandra in the cloud Install and use DCOS for big data processing Use Apache Spark for big data stack data processing Who This Book Is For Developers, architects, IT project managers, database administrators, and others charged with developing or supporting a big data system. It is also for anyone interested in Hadoop or big data, and those experiencing problems with data size.

Giving professionals the edge in aiding children and adolescents with their feelings, this work explains how to incorporate play techniques into therapy, provide group therapy to children, and encourage appropriate parental involvement. Includes handouts and activities.

This newly expanded and revised edition of the Child-Parent Relationship Therapy (CPRT) Treatment Manual is the essential companion to the second edition of Child-Parent Relationship Therapy (CPRT). The second edition is updated to include four new CPRT treatment protocols and parent notebooks adapted for specific populations: parents of toddlers, parents of preadolescents, adoptive families, and the teacher/student relationship, along with the revised original CPRT protocol and parent notebook for ages 3−10. This manual provides the CPRT/filial therapist a comprehensive framework for conducting CPRT. Included are detailed outlines, teaching aides, activities, and resources for each of the 10 sessions. The manual is divided into two major sections, Therapist Protocol and Parent Notebook, and contains a comprehensive CPRT Training Resources section along with an index to the accompanying Companion Website. The accompanying Companion Website contains all necessary and supplemental training materials in a format that allows for ease of reproduction and enhanced usability including the following: CPRT Protocol—Ages 3 to 10 and Parent Notebook Toddler Adapted CPRT Protocol and Parent Notebook Preadolescent Adapted CPRT Protocol and Parent Notebook Adoptive Families Adapted CPRT Protocol and Parent Notebook Teacher/Student Adapted Protocol and Teacher Notebook Therapist Study Guide Training Resources, Teaching Aides and Supplemental Materials Marketing Materials Assessments Drawing on their extensive experience as professional play therapists and filial therapists, Bratton and Landreth apply the principles of CCPT and CPRT in this easy-to-follow protocol for practitioners to successfully implement the evidence-based CPRT model. By using this manual and the accompanying Companion Website in conjunction with the CPRT text, filial therapists will have a complete package for training parents in CCPT skills to act as therapeutic agents with their own children.

Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Summary Deep learning offers the potential to identify complex patterns and relationships hidden in data of all sorts. Deep Learning with Structured Data shows you how to apply powerful deep learning analysis techniques to the kind of structured, tabular data you'll find in the relational databases that real-world businesses depend on. Filled with practical, relevant applications, this book teaches you how deep learning can augment your existing machine learning and business intelligence systems. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Here’s a dirty secret: Half of the time in most data science projects is spent cleaning and preparing data. But there’s a better way: Deep learning techniques optimized for tabular data and relational databases deliver insights and analysis without requiring intense feature engineering. Learn the skills to unlock deep learning performance with much less data filtering, validating, and scrubbing. About the book Deep Learning with Structured Data teaches you powerful data analysis techniques for tabular data and relational databases. Get started using a dataset based on the Toronto transit system. As you work through the book, you’ll learn how easy it is to set up tabular data for deep learning, while solving crucial production concerns like deployment and performance monitoring. What's inside When and where to use deep learning The architecture of a Keras deep learning model Training, deploying, and maintaining models Measuring performance About the reader For readers with intermediate Python and machine learning skills. About the author Mark Ryan is a Data Science Manager at Intact Insurance. He holds a Master's degree in Computer Science from the University of Toronto. Table of Contents 1 Why deep learning with structured data? 2 Introduction to the example problem and Pandas dataframes 3 Preparing the data, part 1: Exploring and cleansing the data 4 Preparing the data, part 2: Transforming the data 5 Preparing and building the model 6 Training the model and running experiments 7 More experiments with the trained model 8 Deploying the model 9 Recommended next steps