Overview
Start prototyping AI applications powered by PyTorch by leveraging popular pretrained models in the fields of Computer Vision and Natural Language Processing covering an extensive span of practical applications.
This course provides hands-on experience to train and fine-tune deep learning models using the rich PyTorch and Hugging Face ecosystems of pre-trained models for Computer Vision and Natural Language Processing tasks. Additionally, you will be able to deploy prototype applications using TorchServe, allowing you to quickly validate and demo your application.
Skills Covered
- Overview of PyTorch, including model classes, datasets, data loaders and the training loop.
- The role and power of transfer learning, along with how to use it with pretrained models.
- Practical lab exercises cover multiple topics including: image classification, object detection, sentiment analysis, text classification, and text generation/completion.
- Learners also will use their data to fine-tune existing models and leverage third-party APIs.
Who Should Attend
- This course is designed for machine learning practitioners who want to add deep learning models in PyTorch – especially pretraining models for Computer Vision and Natural Language Processing – to quickly prototype and deploy applications.
Course Curriculum
Prerequisites
To get the most possible value from this course, you should be familiar with the following:
- Python (notions of Object-Oriented Programming (OOP))
- PyData Stack (Numpy – arrays, slicing, vectorized operations – , Pandas – series, slicing, indexing, transformations – , Matplotlib – basic plotting only – , Scikit-Learn – linear regression, pipelines, one-hot encoding, normalization/scaling, grid search, hyper-parameter optimization)
- Machine Learning Concepts (supervised learning: regression and classification; loss functions: RMSE, cross-entropy; train-validation-test split; evaluation metrics (R-squared, precision, recall, accuracy, confusion matrix)
