Deep Learning Adventures with PyTorch

Journey into the world of deep learning using PyTorch. Recognize images, translate languages, and paint unique pictures

Are you ready to go on a journey into the world of deep learning? This course will be your guide through the joys and dangers of this new wave of machine learning. Why? Because, let's face it, getting started with deep learning is difficult. Tasks such as choosing between multiple frameworks, understanding APIs, and debugging code are hard. Is there an another way? Yes. Meet PyTorch. Like Python, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. This course will be one hell of an adventure into the world of deep learning!

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 code bundle for this video course is available at - https://github.com/PacktPublishing/Deep-Learning-Adventures-with-PyTorch

Style and Approach

In this course, you will complete your deep-learning journey with a trusted guide and use PyTorch to build interesting and useful deep learning projects. In each example you will learn how to solve a specific, practical Machine Learning problem.

What You Will Learn

Intuitive ways to build neural networks using the PyTorch API to make this deep learning ride enjoyable
Master PyTorch's unique features gradually as you work through projects that make PyTorch perfect for rapid prototyping
Debug your PyTorch code using standard Python tools, so you can easily fix bugs
Work with PyTorch and learn its advantages over other frameworks, and choose the right vehicle for your deep-learning ride
Get practical, project-based experience with this popular and in-demand deep-learning library