Laura Mitchell - Deep Learning with PyTorch 1.x.

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First published: February 2018
Second edition: November 2019

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Laura Mitchell graduated with a degree in mathematics from the University of Edinburgh. With 15 years of experience in the tech and data science space, Laura is the lead data scientist at MagicLab whose brands have connected the lives of over 500 million people through dating, social and business. Laura has hands-on experience in the delivery of projects surrounding natural language processing, image classification and recommender systems, from initial conception to production. She has a passion for learning new technologies and keeping herself up to date with industry trends.

Sri. Yogesh K. is an experienced data scientist with a history of working in higher education. He is skilled in Python, Apache Spark, deep learning, Hadoop, and machine learning. He is a strong engineering professional with a Certificate of Engineering Excellence from the International School of Engineering (INSOFE) and is focused on big data analytics. Sri has trained over 500 working professionals in data science and deep learning from companies including Flipkart, Honeywell, GE, and Rakuten. Additionally, he has worked on various projects that involved deep learning and PyTorch.

Vishnu Subramanian has experience in leading, architecting, and implementing several big data analytical projects using artificial intelligence, machine learning, and deep learning. He specializes in machine learning, deep learning, distributed machine learning, and visualization. He has experience in retail, finance, and travel domains. Also, he is good at understanding and coordinating between businesses, AI, and engineering teams.

Mingfei Ma is a senior deep learning software engineer from Intel Asia-Pacific Research & Development Ltd and he has plenty of experience in high-performance computation. Mingfei contributed extensively to the CPU performance optimization of PyTorch and its predecessor, Torch. He also has expertise in computer graphics, heterogeneous computing, microarchitecture detection, high-performance computation libraries, and more.

Ajit Pratap Kundan is at the forefront of innovative technologies in the world of IT. He's worked with HPE, VMware, Novell, Redington, and PCS to help their customers in transforming their data centers through software-defined services. Ajit is an innovative pre-sales tech enthusiast with over 19 years of experience in technologies such as Lotus, SUSE Linux, Platespin, and all VMware solutions. Ajit is a valued author on cloud technologies and has authored two books, VMware Cross-Cloud Architecture and Intelligent Automation with VMware, published by Packt.

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PyTorch is grabbing the attention of deep learning researchers and data science professionals due to its accessibility, efficiency, and the fact of it being more native to the Python way of development. This book will get you up and running with one of the most cutting-edge deep learning librariesPyTorch.

In this second edition, you'll learn about the various fundamental building blocks that power modern deep learning using the new features and offerings of the PyTorch 1.x library. You will learn how to solve real-world problems using convolutional neural networks (CNNs), recurrent neural networks (RNNs), and