We humans generate artwork with different levels of accuracy and complexity. Though the process of creating art could be a very complex process, it can be seen as a combination of the two most important factors, namely, what to draw and how to draw. What to draw is inspired by what we see around us, and how we draw will also take influences from certain things that are found around us. This could be an oversimplification from an artist's perspective, but for understanding how we can create artwork using deep learning algorithms, it is very useful. We will train a deep learning algorithm to take content from one image and then draw it according to a specific artistic style. If you are an artist or in the creative industry, you can directly use the amazing research that has gone on in recent years to improve this and create something cool within the domain...
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Book Overview & Buying
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Table Of Contents
Deep Learning with PyTorch
By :
Deep Learning with PyTorch
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Overview of this book
Deep learning powers the most intelligent systems in the world, such as Google Voice, Siri, and Alexa. Advancements in powerful hardware, such as GPUs, software frameworks such as PyTorch, Keras, TensorFlow, and CNTK along with the availability of big data have made it easier to implement solutions to problems in the areas of text, vision, and advanced analytics.
This book will get you up and running with one of the most cutting-edge deep learning libraries—PyTorch. PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency and being more native to Python way of development. You'll start off by installing PyTorch, then quickly move on to learn various fundamental blocks that power modern deep learning. You will also learn how to use CNN, RNN, LSTM and other networks to solve real-world problems. This book explains the concepts of various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math behind them. You will also learn about GPU computing during the course of the book. You will see how to train a model with PyTorch and dive into complex neural networks such as generative networks for producing text and images.
By the end of the book, you'll be able to implement deep learning applications in PyTorch with ease.
Table of Contents (11 chapters)
Preface
Free Chapter
Getting Started with Deep Learning Using PyTorch
Building Blocks of Neural Networks
Diving Deep into Neural Networks
Fundamentals of Machine Learning
Deep Learning for Computer Vision
Deep Learning with Sequence Data and Text
Generative Networks
Modern Network Architectures
What Next?
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