PyTorch Learning Path
Learn how to develop deep learning models in PyTorch with Python
Getting Started with PyTorch
Welcome to the “Getting Started with PyTorch” section! This module is your launchpad into the world of PyTorch, the dynamic open-source framework for deep learning. From grasping core tensor concepts to constructing your initial neural network, this section equips you with vital skills for your AI and machine learning endeavors. Let’s dive in and unleash the power of PyTorch!
Get an entire overview of how to develop deep learning models in PyTorch using an end-to-end project.
Working with Data in PyTorch
Within this module, you’ll master the art of handling data using the PyTorch framework. From importing datasets to creating efficient data loaders, you’ll gain essential skills for preprocessing, augmenting, and managing data flows in the context of deep learning. Join us in this journey to elevate your data manipulation prowess and amplify your PyTorch capabilities!
Understand the fundamental building blocks of PyTorch models: the PyTorch Tensor.
Learn how to structure your data using PyTorch Datasets, which add helpful methods to your data.
Learn how to easily batch your deep learning workflow by using DataLoaders to create iterables.
Learn how PyTorch implements automatic differentiation using the autograd package, allowing you to easily backpropagate through your models to update their parameters.
Transform Your Data with PyTorch
This module delves into the powerful techniques of data augmentation, normalization, and custom transformation pipelines within the PyTorch framework. By mastering these skills, you’ll enhance the quality and diversity of your data, leading to more robust and effective machine learning models. Let’s embark on this transformative journey and unlock the full potential of your data using PyTorch!
Transform your data in PyTorch using common transformations in structured ways.
Learn about how PyTorch activation functions allow you to find non-linear patterns in your data.
The Rectified Linear Unit (ReLU) function is a cornerstone activation function, enabling simple, neural efficiency for reducing the impact of the vanishing gradient problem.
The softmax activation function is particularly useful for multi-class classification tasks, such as those in computer vision problems.
The Tanh activation function is particularly useful for recurrent neural networks or long short-term memory networks.
Loss Function in PyTorch
In this module, you’ll develop a strong understanding of loss functions in deep learning. You’ll get to understand both the role that they play in deep learning, as well as the various loss functions that PyTorch makes available. Loss functions allow your model to learn, by identifying what’s working well and, importantly, what’s not.
Understand the role of loss functions in deep learning and get an understanding of how they’re used in PyTorch.
Cross-entropy loss is one of the most common loss functions for multi-class classification tasks. Learn how to implement this powerful function in PyTorch.
Learn about the Mean Absolute Error (MAE) or L1 Loss Function in PyTorch for developing your deep-learning models. The MAE loss function is an important criterion for evaluating regression models in PyTorch.
Learn about the Mean Squared Error (MSE) or L2 Loss Function in PyTorch for developing your deep-learning models. The MSE loss function is an important criterion for evaluating regression models in PyTorch.
Building Deep Learning Models with PyTorch
In this module, you’ll ascend to the next level of expertise by mastering the creation and training of deep learning models using PyTorch. From designing intricate neural architectures to optimizing their performance, you’ll gain hands-on experience in crafting cutting-edge solutions for a variety of tasks. Join us on this exciting journey to unlock the art of building powerful and innovative deep learning models with PyTorch!
Develop linear regression models in PyTorch using streamlined approaches.
Build computer vision models using a hands-on tutorial on convolutional neural networks.
Learn how to quickly build deep learning models by standing on the shoulders of giants (or at least the models they’ve already built!).