In the example of image classification that we covered in the last chapter, we split the data into two different halves, one for training and one for validation. It is a good practice to use a separate dataset to test the performance of your algorithm, as testing the algorithm on the training set may not give you the true generalization power of the algorithm. In most real-world use cases, based on the validation accuracy, we often tweak our algorithm in different ways, such as adding more layers or different layers, or using different techniques that we will cover in the later part of the chapter. So, there is a higher chance that your choices for tweaking the algorithm are based on the validation dataset. Algorithms trained this way tend to perform well in the training dataset and the validation dataset, but fail to generalize well on unseen...
Deep Learning with PyTorch
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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.
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Current Title:
Deep Learning with PyTorch
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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