One of the major difficulties with image classification models is the lack of labeled data. It is difficult to assemble a labeled dataset of sufficient size to train a model well; it is an extremely time consuming and laborious task. This is not such a problem for MNIST, since the images are relatively simple. They are greyscale and largely consist only of target features, there are no distracting background features, and the images are all aligned the same way and are of the same scale. A small dataset of 60,000 images is quite sufficient to train a model well. It is rare to find such a well-organized and consistent dataset in the problems we encounter in real life. Images are often of variable quality, and the target features can be obscured or distorted. They can also be of widely variable scales and rotations. The solution is to use a model architecture that...
Deep Learning with PyTorch Quick Start Guide
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Deep Learning with PyTorch Quick Start Guide
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Overview of this book
PyTorch is extremely powerful and yet easy to learn. It provides advanced features, such as supporting multiprocessor, distributed, and parallel computation. This book is an excellent entry point for those wanting to explore deep learning with PyTorch to harness its power.
This book will introduce you to the PyTorch deep learning library and teach you how to train deep learning models without any hassle. We will set up the deep learning environment using PyTorch, and then train and deploy different types of deep learning models, such as CNN, RNN, and autoencoders.
You will learn how to optimize models by tuning hyperparameters and how to use PyTorch in multiprocessor and distributed environments. We will discuss long short-term memory network (LSTMs) and build a language model to predict text.
By the end of this book, you will be familiar with PyTorch's capabilities and be able to utilize the library to train your neural networks with relative ease.
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Current Title:
Deep Learning with PyTorch Quick Start Guide
Table of Contents (8 chapters)
Preface
Free Chapter
Introduction to PyTorch
Deep Learning Fundamentals
Computational Graphs and Linear Models
Convolutional Networks
Other NN Architectures
Getting the Most out of PyTorch
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