Deep Learning Adventures with PyTorch [Video]
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-PyTorchStyle 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.
|Course Length||2 hours 31 minutes|
|Date Of Publication||31 Oct 2018|
|Quick Win: Using a Pretrained AlexNet Model for Beaver Detection|
|Getting and Preparing Image Data|
|Building, Training, and Testing Your Model|
|Using Your Model to Detect Beavers and What’s Next?|
|Understanding and Preparing Language Data|
|Building, Training, and Testing Your Model for Language Detection|
|Using Your Model to Detect Languages and What’s Next?|