Julian - Deep learning with PyTorch quick start guide learn to train and deploy neural network models in Python

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David Julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultural environments (Urban Ecological Systems Ltd., Bluesmart Farms), designing and implementing event management data systems (Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations (Adelaide University). He has also written Designing Machine Learning Systems With Python for Packt Publishing and was technical reviewer for Python Machine Learning and Hands-On Data Structures and Algorithms with Python - Second Edition, published by Packt.

AshishSingh Bhatia has more than 10 years' IT experience in different domains, including ERP, banking, education, and resource management. He is a learner, reader, and developer at heart. He is passionate about Python, Java, and R. He loves to explore new technologies. He has also published two books: Machine Learning with Java and R and Natural Language Processing with Java. Apart from this, he has also recorded a video tutorial on PyTorch.

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PyTorch is surprisingly easy to learn and provides advanced features such as a supporting multiprocessor, as well as distributed and parallel computation. PyTorch has a library of pre-trained models, providing out-of-the-box solutions for image classification. PyTorch offers one of the most accessible entry points into cutting-edge deep learning. It is tightly integrated with the Python programming language, so for Python programmers, coding it seems natural and intuitive. The unique, dynamic way of treating computational graphs means that PyTorch is both efficient and flexible.

This book is for anyone who wants a straightforward, practical introduction to deep learning using PyTorch. The aim is to give you an understanding of deep learning models by direct experimentation. This book is perfect for those who are familiar with Python, know some machine learning basics, and are looking for a way to productively develop their skills. The book will focus on the most important features and give practical examples. It assumes you have a working knowledge of Python and are familiar with the relevant mathematical ideas, including with linear algebra and differential calculus. The book provides enough theory to get you up and running without requiring rigorous mathematical understanding. By the end of the book, you will have a practical knowledge of deep learning systems and able to apply PyTorch models to solve the problems that you care about.

, Introduction to PyTorch , gets you up and running with PyTorch, demonstrates its installation on a variety of platforms, and explores key syntax elements and how to import and use data in PyTorch.

, Deep Learning Fundamentals , is a whirlwind tour of the basics of deep learning, covering the mathematics and theory of optimization, linear networks, and neural networks.

, Computational Graphs and Linear Models , demonstrates how to calculate the error gradient of a linear network and how to harness it to classify images.

, Convolutional Networks , examines the theory of convolutional networks and how to use them for image classification.

, Other NN Architectures