Deep Learning with PyTorch

Build neural network models in text vision and advanced analytics using PyTorchKey FeaturesLearn 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;Book DescriptionDeep 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. Youll 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 youll be able to implement deep learning applications in PyTorch with ease.What you will learnUse PyTorch for GPU-accelerated tensor computationsBuild custom datasets and data loaders for images and test the models using torchvision and torchtextBuild an image classifier by implementing CNN architectures using PyTorchBuild systems that do text classification and language modeling using RNN LSTM and GRULearn advanced CNN architectures such as ResNet Inception Densenet and learn how to use them for transfer learningLearn how to mix multiple models for a powerful ensemble modelGenerate new images using GAN’s and generate artistic images using style transferWho this book is forThis 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.