PyTorch Essentials: An Applications-First Approach (LFD273)

Course code: LFD273

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.

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Course dates

Starting date: Upon request

Type: Self-paced

Course duration: 365 days

Language: en

Price without VAT: 285 EUR


Type Course
Language Price without VAT
Upon request Self-paced 365 days en 285 EUR Register
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Course description

The course begins with an overview of PyTorch, including model classes, datasets, data loaders and the training loop. Next, it covers 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.

This course includes
  • Online, Self Paced
  • 40 Hours of Course Material
  • Hands-on Labs & Assignments1
  • 12 Months of Access to Online Course
  • Digital Badge
  • Discussion forums

Target group

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 structure

  • Chapter 1. Course Introduction
  • Chapter 2. PyTorch, Datasets, and Models
  • Chapter 3. Building Your First Dataset
  • Chapter 4. Training Your First Model
  • Chapter 5. Building Your First DataPipe
  • Chapter 6. Transfer Learning and Pretrained Models
  • Chapter 7. Pretrained Models for Computer Vision
  • Chapter 8. Pretrained Models for Natural Language Processing
  • Chapter 9. Image Classification with Torchvision
  • Chapter 10. Fine-Tuning Pretrained Models for Computer Vision
  • Chapter 11. Serving Models with TorchServe
  • Chapter 12. Datasets and Transformations for Object Detection and Image Segmentation
  • Chapter 13. Models for Object Detection and Image Segmentation
  • Chapter 14. Object Detection Evaluation
  • Chapter 15. Word Embeddings and Text Classification
  • Chapter 16. Contextual Word Embeddings with Transformers
  • Chapter 17. Hugging Face Pipelines for NLP Tasks
  • Chapter 18. Q&A, Summarization, and LLMs


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)

To do the lab exercises in this course, you’ll need the following:

  • Google account (for Google Colab, free tier)

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