Deep Learning An Introduction With Two Nice Short Examples
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Deep Learning, an introduction with two nice short examples
Author: Roland Büchi
language: en
Publisher: BoD – Books on Demand
Release Date: 2022-09-23
This booklet introduces the most important basics of deep learning. It describes the very frequently used method of how a computer can learn using neural networks and training data and apply what it has learned to other questions similar to the training data. Two simple about 'one-pager' examples in Python show how training a neural network with forward and back propagation works and how the trained system can process simple forms of artificial thinking. The two short Python programs "Learning truth tables" and "Recognizing a questionnaire" are printed in full and are easy to follow.
Machine Learning for Kids
A hands-on, application-based introduction to machine learning and artificial intelligence (AI). Create compelling AI-powered games and applications using the Scratch programming language. AI Made Easy with 13 Projects Machine learning (also known as ML) is one of the building blocks of AI, or artificial intelligence. AI is based on the idea that computers can learn on their own, with your help. Machine Learning for Kids will introduce you to machine learning, painlessly. With this book and its free, Scratch-based companion website, you’ll see how easy it is to add machine learning to your own projects. You don’t even need to know how to code! Step by easy step, you’ll discover how machine learning systems can be taught to recognize text, images, numbers, and sounds, and how to train your models to improve them. You’ll turn your models into 13 fun computer games and apps, including: A Rock, Paper, Scissors game that recognizes your hand shapes A computer character that reacts to insults and compliments An interactive virtual assistant (like Siri or Alexa) A movie recommendation app An AI version of Pac-Man There’s no experience required and step-by-step instructions make sure that anyone can follow along! No Experience Necessary! Ages 12+
Deep Learning for Coders with fastai and PyTorch
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