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Presents more than four hundred authentic recipes from countries around the world along with information on land, history, food, and cooking techniques for each region.
"This guide outlines the official curriculum and instructional policies of the Pasadena City Schools as approved by the Board of Education."--Page [3].
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This handbook is written for any student between the ages of 15 and 19 studying Chemistry. Its content meets the core chemistry requirements of IGCSE, IBDP, A-Level and AP courses. The material will also help an undergraduate whose course requires a basic foundation in Chemistry. It offers an alternative, succinct perspective to enable students to understand key concepts and can be used as a concise reference resource or a review guide. Each topic contains comprehensive explanations supported by diagrams and worked examples. The final sections of the book hold useful reference material for experimental work and offer guidance on how to write laboratory reports. There is also a series of practice calculation questions with solutions.
Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala
How to homeschool through junior and senior high school. Covers quick start, setting goals, required areas of study, graduation requirements, course descriptions, and more. Includes an appendix for California residents.
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.
A guide to getting through high school and deciding what will come next, providing information about courses, grades, testing, communicating with teachers, and postgraduation options.