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