Practical Guide to Deep Learning with PyTorch

PyTorch: written in Python, is grabbing the attention of all data science professionals due to its ease of use over other libraries and its use of dynamic computation graphs. PyTorch is a Deep Learning framework that is a boon for researchers and data scientists. It supports Graphic Processing Units and is a platform that provides maximum flexibility and speed. With PyTorch, you can dynamically build neural networks and easily perform advanced Artificial Intelligence tasks.

This comprehensive 3-in-1 course takes a practical approach and is filled with real-world examples to help you create your own application using PyTorch! You’ll begin by designing and implementing powerful neural networks to solve some impressive problems in a step-by-step manner. Build a Convolutional Neural Network (CNN) for image recognition. Next, you’ll apply Deep Learning architectures to solve Machine Learning problems for Computer Vision, and Natural Language Processing. Finally, You’ll debug your PyTorch code using standard Python tools, so you can easily fix bugs and detect credit card fraud with autoencoders and much more!

By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework with the help of real-world examples!

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

The first course, PyTorch Deep Learning in 7 Days, covers seven short lessons and a daily exercise, carefully chosen to get you started with PyTorch Deep Learning faster than other courses. This 7-day course is for those who are in a hurry to get started with PyTorch. You will be introduced to the most commonly used Deep Learning models, techniques, and algorithms through PyTorch code. This course is an attempt to break the myth that Deep Learning is complicated and show you that with the right choice of tools combined with a simple and intuitive explanation of core concepts, Deep Learning is as accessible as any other application development technologies out there. It’s a journey from diving deep into the fundamentals to getting acquainted with the advanced concepts such as Transfer Learning, Natural Language Processing and implementation of Generative Adversarial Networks. By the end of the course, you will be able to build Deep Learning applications with PyTorch

The second course, Deep Learning Adventures with PyTorch, covers Deep Learning using PyTorch. Recognize images, translate languages, and paint unique pictures. You'll start by using Convolutional Neural Networks (CNNs) to classify images; Recurrent Neural Networks (RNNs) to detect languages, and then translate them using Long-Term-Short Memory (LTSM). Finally, you'll channel your inner Picasso by using Deep Neural Network (DNN) to paint unique images. By the end of your adventure, you will be ready to use PyTorch proficiently in your real-world projects.

The third course, Deep Learning Projects with PyTorch, covers creating deep learning models with the help of real-world examples. The course starts with the fundamentals of PyTorch and how to use basic commands. Next, you’ll learn about Convolutional Neural Networks (CNN) through an example of image recognition, where you’ll look into images from a machine perspective. The next project shows you how to predict character sequence using Recurrent Neural Networks (RNN) and Long Short Term Memory Network (LSTM). Then you’ll learn to work with autoencoders to detect credit card fraud. After that, it’s time to develop a system using Boltzmann Machines, where you’ll recommend whether to watch a movie or not. By the end of the course, you’ll be able to start using PyTorch to build Deep Learning models by implementing practical projects in the real world. So, grab this course as it will take you through interesting real-world projects to train your first neural nets.

By the end of the course, you’ll conquer the world of PyTorch to build useful and effective Deep Learning models with the PyTorch Deep Learning framework!

About the Authors

• Will Ballard is the chief technology officer at GLG, responsible for engineering and IT. He was also responsible for the design and operation of large data centers that helped run site services for customers including Gannett, Hearst Magazines, NFL, NPR, The Washington Post, and Whole Foods. He has also held leadership roles in software development at NetSolve (now Cisco), NetSpend, and Works (now Bank of America).

• Jakub Konczyk has enjoyed programming professionally since 1995. He is a Python and Django expert and has been involved in building complex systems since 2006. He loves to simplify and teach programming subjects and share them with others. He first discovered Machine Learning when he was trying to predict real estate prices in one of the early stage startups he was involved in. He failed miserably. Then he discovered a much more practical way to learn Machine Learning, which he would like to share with you in this course. It boils down to the Keep it simple! mantra.

• AshishSingh Bhatia is a learner, reader, seeker, and developer at the core. He has over 10 years of IT experience in different domains, including banking, ERP, and education. He is persistently passionate about Python, Java, R, and web and mobile development. He is always ready to explore new technologies.