Draft Energy Bill

Draft Energy Bill
Author: Great Britain: Parliament: House of Commons: Energy and Climate Change Committee
Publisher: The Stationery Office
Total Pages: 254
Release: 2012-07-23
Genre: Political Science
ISBN: 9780215047274

Additional written evidence is contained in Volume 3, available on the Committee website at www.parliament.uk/ecc

How to Avoid a Climate Disaster

How to Avoid a Climate Disaster
Author: Bill Gates
Publisher: Knopf Canada
Total Pages: 240
Release: 2021-02-16
Genre: Science
ISBN: 0735280452

NEW YORK TIMES BESTSELLER NATIONAL BESTSELLER In this urgent, singularly authoritative book, Bill Gates sets out a wide-ranging, practical--and accessible--plan for how the world can get to zero greenhouse gas emissions in time to avoid an irreversible climate catastrophe. Bill Gates has spent a decade investigating the causes and effects of climate change. With the help and guidance of experts in the fields of physics, chemistry, biology, engineering, political science and finance, he has focused on exactly what must be done in order to stop the planet's slide toward certain environmental disaster. In this book, he not only gathers together all the information we need to fully grasp how important it is that we work toward net-zero emissions of greenhouse gases but also details exactly what we need to do to achieve this profoundly important goal. He gives us a clear-eyed description of the challenges we face. He describes the areas in which technology is already helping to reduce emissions; where and how the current technology can be made to function more effectively; where breakthrough technologies are needed, and who is working on these essential innovations. Finally, he lays out a concrete plan for achieving the goal of zero emissions--suggesting not only policies that governments should adopt, but what we as individuals can do to keep our government, our employers and ourselves accountable in this crucial enterprise. As Bill Gates makes clear, achieving zero emissions will not be simple or easy to do, but by following the guidelines he sets out here, it is a goal firmly within our reach.

Energy Savers

Energy Savers
Author:
Publisher:
Total Pages: 0
Release: 2014
Genre:
ISBN:

Provides consumers with home energy and money savings tips such as insulation, weatherization, heating, cooling, water heating, energy efficient windows, landscaping, lighting, and energy efficient appliances.

How to Draft Bills Clients Rush to Pay

How to Draft Bills Clients Rush to Pay
Author: J. Harris Morgan
Publisher: American Bar Association
Total Pages: 152
Release: 2003
Genre: Law
ISBN:

This is a step-by step guide on drafting and formatting invoices that clients will fully understand, find reasonable, and be more likely to pay-on time and without complaint.

Model Rules of Professional Conduct

Model Rules of Professional Conduct
Author: American Bar Association. House of Delegates
Publisher: American Bar Association
Total Pages: 216
Release: 2007
Genre: Law
ISBN: 9781590318737

The Model Rules of Professional Conduct provides an up-to-date resource for information on legal ethics. Federal, state and local courts in all jurisdictions look to the Rules for guidance in solving lawyer malpractice cases, disciplinary actions, disqualification issues, sanctions questions and much more. In this volume, black-letter Rules of Professional Conduct are followed by numbered Comments that explain each Rule's purpose and provide suggestions for its practical application. The Rules will help you identify proper conduct in a variety of given situations, review those instances where discretionary action is possible, and define the nature of the relationship between you and your clients, colleagues and the courts.

Mathematics for Machine Learning

Mathematics for Machine Learning
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
Total Pages: 392
Release: 2020-04-23
Genre: Computers
ISBN: 1108569323

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.