Deep Learning with PyTorch – Vishnu Subramanian

PyTorch is grabbing the attention of data science professionals and deep learning practitioners due to its flexibility and ease of use. This book introduces the fundamental building blocks of deep learning and PyTorch. It demonstrates how to solve real-world problems using a practical approach. You will also learn some of the modern architectures and techniques that are used to crack some cutting- edge research problems.

This book provides the intuition behind various state-of-the-art deep learning architectures, such as ResNet, DenseNet, Inception, and Seq2Seq, without diving deep into the math. It also shows how to do transfer learning, how to speed up transfer learning using pre-computed features, and how to do text classification using embeddings, pretrained embeddings, LSTM, and one-dimensional convolutions.

By the end of the book, you will be a proficient deep learning practitioner who will be able to solve some business problems using the different techniques learned here.

Related posts:

Deep Learning with Applications Using Python - Navin Kumar Manaswi
R Deep Learning Essentials - Dr. Joshua F.Wiley
Python Machine Learning Eqution Reference - Sebastian Raschka
Natural Language Processing in action - Hobson Lane & Cole Howard & Hannes Max Hapke
Machine Learning Mastery with Python - Understand your data, create accurate models and work project...
Machine Learning Applications Using Python - Cases studies form Healthcare, Retail, and Finance - Pu...
Deep Learning with Hadoop - Dipayan Dev
Python Machine Learning Third Edition - Sebastian Raschka & Vahid Mirjalili
Scikit-learn Cookbook Second Edition over 80 recipes for machine learning - Julian Avila & Trent Hau...
Machine Learning - An Algorithmic Perspective second edition - Stephen Marsland
Deep Learning with Python - A Hands-on Introduction - Nikhil Ketkar
Introduction to the Math of Neural Networks - Jeff Heaton
Machine Learning with spark and python - Michael Bowles
Deep Learning dummies second edition - John Paul Mueller & Luca Massaronf
Natural Language Processing Recipes - Akshay Kulkni & Adarsha Shivananda
Machine Learning - The art and science of alhorithms that make sense of data - Peter Flach
Introduction to Deep Learning Business Application for Developers - Armando Vieira & Bernardete Ribe...
Understanding Machine Learning from theory to algorithms - Shai Shalev-Shwartz & Shai Ben-David
Superintelligence - Paths, Danges, Strategies - Nick Bostrom
Coding Theory - Algorithms, Architectures and Application
Python Deep Learning Cookbook - Indra den Bakker
Fundamentals of Deep Learning - Nikhil Bubuma
Amazon Machine Learning Developer Guild Version Latest
Python Data Structures and Algorithms - Benjamin Baka
Python Deeper Insights into Machine Learning - Sebastian Raschka & David Julian & John Hearty
Pattern recognition and machine learning - Christopher M.Bishop
Python Artificial Intelligence Project for Beginners - Joshua Eckroth
Python Deep Learning - Valentino Zocca & Gianmario Spacagna & Daniel Slater & Peter Roelants
Pro Deep Learning with TensorFlow - Santunu Pattanayak
Deep Learning with Python - Francois Chollet
Deep Learning - A Practitioner's Approach - Josh Patterson & Adam Gibson
Intelligent Projects Using Python - Santanu Pattanayak