Build neural network models in text, vision and advanced analytics using PyTorch

About This Book

• Learn PyTorch for implementing cutting-edge deep learning algorithms.
• Train your neural networks for higher speed and flexibility and learn how to implement them in various scenarios;
• Cover various advanced neural network architecture such as ResNet, Inception, DenseNet and more with practical examples;

Who This Book Is For

This book is for machine learning engineers, data analysts, data scientists interested in deep learning and are looking to explore implementing advanced algorithms in PyTorch. Some knowledge of machine learning is helpful but not a mandatory need. Working knowledge of Python programming is expected.

What You Will Learn

• Use PyTorch for GPU-accelerated tensor computations
• Build custom datasets and data loaders for images and test the models using torchvision and torchtext
• Build an image classifier by implementing CNN architectures using PyTorch
• Build systems that do text classification and language modeling using RNN, LSTM, and GRU
• Learn advanced CNN architectures such as ResNet, Inception, Densenet, and learn how to use them for transfer learning
• Learn how to mix multiple models for a powerful ensemble model
• Generate new images using GAN's and generate artistic images using style transfer

In Detail

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.

Style and approach

An end-to-end guide that teaches you all about PyTorch and how to implement it in various scenarios.

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