This fantastic Functional Skills book has everything students need to prepare for the Level 2 Maths test! It covers every exam board and every topic, including all the calculator and non-calculator skills needed for the latest L2 Functional Skills specifications. Everything's explained in CGP's easy-to-understand style, with examples and notes galore. Each topic is followed by a page of practice questions, so students can learn then test themselves as they go. We've also included exam-style practice papers with full answers so you'll know what to expect on the big day. Nice!

This essential Functional Skills English book has everything students need to succeed in the Level 2 English tests! It covers every exam board and every topic and skill from the L2 Functional Skills Reading and Writing specifications. Everything's explained in CGP's easy-to-understand style, with examples and notes galore. Each topic is followed by a page of practice questions, so you can learn then test yourself as you go. We've included exam-style practice papers for Reading and Writing with full answers — so you'll know what to expect on the big day. Smashing!

This book is an ideal way to prepare for the Level 1 English Functional Skills test - whichever exam board you're studying. Each topic is clearly explained with straightforward notes, tips and examples. There are also practice questions throughout the book, plus plenty of test-style questions (with answers) to help you prepare for the real thing.

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.

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