PyTorch Fundamentals
Learn the fundamentals of deep learning with PyTorch! This beginner friendly learning path will introduce key concepts to building machine learning models in multiple domains include speech, vision, and natural language processing.
Prerequisites
- Basic Python knowledge
- Basic knowledge about how to use Jupyter Notebooks
- Basic understanding of machine learning
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Modules in this learning path
Learn key concepts used to build machine learning models with PyTorch. We'll train a neural network model that recognizes and classifies images.
We'll learn about different computer vision tasks and focus on image classification, learning how to use neural networks to classify handwritten digits, as well as some real-world images, such as photographs of cats and dogs. We'll be using one of the most popular deep learning frameworks, PyTorch!
In this module, we will explore different neural network architectures for dealing with natural language texts. In the recent years, Natural Language Processing (NLP) has experienced fast growth primarily due to the performance of the language models’ ability to accurately "understand" human language faster while using unsupervised training on large text corpora. We will learn about different NLP techniques such as using bag-of-words (BoW), word embeddings and recurrent neural networks for classifying text from news headlines to one of the 4 categories (World, Sports, Business and Sci-Tech).
In this Learn module, you learn how to do audio classification with PyTorch. You'll understand more about audio data features and how to transform the sound signals into a visual representation called spectrograms. Then you'll build the model by using computer vision on the spectrogram images. That's right, you can turn audio into an image format, and then do computer vision to classify the word spoken!