HackSpace Magazine: Issue 58
As children we were told not to play with our food. As adults, we can do whatever the heck we like. Join us this month to celebrate the joy of messing around with food, whether that's grilling cheese, making coffee, or automating the construction of tacos. - Find things to make and do with polystyrene - Drool over a 3D printed chocolate Jean-Luc Picard - Make magical curiosities with double hinges - Keep food fresh with 3D printed teeth ...
As children we were told not to play with our food. As adults, we can do whatever the heck we like. Join us this month to celebrate the joy of messing around with food, whether that's grilling cheese, making coffee, or automating the construction of tacos. - Find things to make and do with polystyrene - Drool over a 3D printed chocolate Jean-Luc Picard - Make magical curiosities with double hinges - Keep food fresh with 3D printed teeth ...
Numerical Methods for Ordinary Differential Equations
In this book we discuss several numerical methods for solving ordinary differential equations. We emphasize the aspects that play an important role in practical problems. We con?ne ourselves to ordinary differential equations with the exception of the last chapter in which we discuss the heat equation, a parabolic partial differential equation. The techniques discussed in the intro-ductory chapters, for instance interpolation, numerical quadrature and the solution to nonlinear equations, may also be used outside the context of differential equations. They have been in-cluded to make the book self-contained as far as the numerical aspects are concerned. Chapters, sections and exercises marked with a * are not part of the Delft Institutional Package. The numerical examples in this book were implemented in Matlab, but also Python or any other programming language could be used. A list of references to background knowledge and related literature can be found at the end of this book. ...
In this book we discuss several numerical methods for solving ordinary differential equations. We emphasize the aspects that play an important role in practical problems. We con?ne ourselves to ordinary differential equations with the exception of the last chapter in which we discuss the heat equation, a parabolic partial differential equation. The techniques discussed in the intro-ductory chapters, for instance interpolation, numerical quadrature and the solution to nonlinear equations, may also be used outside the context of differential equations. They have been in-cluded to make the book self-contained as far as the numerical aspects are concerned. Chapters, sections and exercises marked with a * are not part of the Delft Institutional Package. The numerical examples in this book were implemented in Matlab, but also Python or any other programming language could be used. A list of references to background knowledge and related literature can be found at the end of this book. ...
Generative Deep Learning, 2nd Edition
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...
Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos; Train GANs to generate images based on your own dataset; Build diffusion models to produce new varieties of flowers; Train your own GPT for text generation; Learn how large language models like ChatGPT are trained; Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN; Compose polyphonic music using Transformers and Mu ...