%run _help_reading.pyimport pandas as pddf = pd.read_csv('https://github.com/MrGeislinger/victorsothervector/raw/main/''data/reading/all_reading-clean.csv')book_name ="""Generative Deep Learning, 2nd Ed"""one_title = one_title_data(df, book_name)one_title_summary = get_summary_by_day(one_title)generate_plot(one_title_summary, book_name);
/Users/victor/Developer/Workspace/victorsothervector/books/_help_reading.py:78: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. Otherwise, ticks may be mislabeled.
ax.set_xticklabels(
Figure 1: Reading done for Generative Deep Learning, 2nd Ed
title: “Generative Deep Learning, 2nd Ed” book: title: “Generative Deep Learning, 2nd Ed” format: html: code-fold: true creator: “@victorgeislinger” —
Thoughts on Generative Deep Learning, 2nd Ed
Overview
Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN
